Quantum-inspired evolutionary computation offers a practical approach to complex optimization by simulating quantum principles on classical systems. This study proposes a multi-objective weighted portfolio model (MoWPM) based on trend ratio evaluation, along with a multi-objective quantum-inspired tabu search algorithm (MoQTS) for portfolio allocation. MoQTS incorporates superposition and an enhanced entanglement mechanism, which effectively improves convergence and expands the Pareto front. Experimental results indicate that the proposed method performs robustly and shows strong potential in supporting diverse financial decision-making needs.

Quantum-inspired metaheuristics have recently demonstrated strong potential in addressing complex optimization problems. Portfolio optimization is a representative real-world task involving two conflicting objectives: maximizing expected return and minimizing risk. Achieving minimum risk in portfolio optimization is a particularly challenging task, as it involves careful analysis of complex interactions among multiple stocks. To address this, this study introduces an enhanced variant of the Multi-objective Quantum-inspired Tabu Search algorithm, incorporating a hierarchical directional strategy that prioritizes exploration from the lowest-risk solutions. Intermediate non-dominated solutions are retained along this trajectory, and an entanglement move mechanism is subsequently applied to further expand the non-dominated solution set from both the high-return and low-risk ends. Experimental results in a cross-market setting involving the U.S. and Japan highlight the proposed model’s promising ability to navigate the enlarged decision space efficiently, delivering competitive results across standard multi-objective metrics with reduced runtime and computational cost. Overall, the proposed method effectively supports cross-market investment decisions with enhanced diversity and improved risk–return trade-offs.

Quantum-inspired evolutionary computation integrates quantum mechanics with classical optimization techniques, offering innovative solutions to complex real-world problems and gaining attention. Among them, portfolio optimization presents a critical issue. Enhancing real-world applicability requires further consideration of fund allocation alongside various investor risk preferences. However, traditional methods face challenges in handling high-dimensional, multi-objective optimization, particularly in maintaining solution diversity and offering interpretable results. To address this challenge, this study proposes a multi-objective weighted portfolio model (MoWPM) incorporating trend ratio-based evaluation. The multi-objective quantum-inspired tabu search (MoQTS) designs for MoWPM, leveraging superposition and enhanced entanglement mechanisms to explore the Pareto front. MoQTS enables the generation of high-quality solutions tailored to various risk levels. In-depth empirical analysis shows that MoQTS and MoWPM outperform equal-weighted approaches, demonstrating robustness across various performance indicators and through statistical validation. Experiments with classical multi-objective algorithms, supported by clear visualizations, underscore the strong potential of quantum-inspired techniques for practical financial optimization.

Portfolio strategy search is an essential topic in the financial and economic networks. Various risk preferences of investors should be considered to accommodate their investment choices, thereby introducing multi-objective portfolio optimization. This study utilizes an innovative and accurate evaluation model, the trend ratio (TR), to assess return and risk and construct a multi-objective optimization (MOO) model. Within a huge search space, computational intelligence techniques achieve high-performance computing through effective nature-inspired computing. The proposed TR-based MOO quantum-inspired model with an entanglement-enhanced local search (EL) mech-anism can search for Pareto optimal solutions (POSs) in global investment markets. The experimental results demonstrate that the TR-based EL mechanism effectively achieves a larger number of POSs and improves efficient frontiers to meet the different risk profiles of investors across global markets. A visualized Pareto front (PF) of the TR model helps a user identify a more straightforward return and risk balance evaluation, helping investors make informed decisions. The entanglement-simulated local search mechanism has been proven to improve the search for a better PF significantly. The TR-based, quantum-inspired MOO portfolio strategy search has paved the way for flexible solutions in global financial networks.

Hybrid quantum computing (QC) combines classical and quantum resources to tackle challenging optimization problems, leveraging the strengths of both within the current constraints of quantum computers, which are limited in size and power. Therefore, we explore the efficacy of a hybrid quantum annealing (QA) search algorithm in improving portfolio optimization. This study pioneers the application of the trend ratio (TR) to a quantum annealing computer, converting it into a constrained quadratic model with a flexible presentation. The TR serves as a promising indicator in portfolio evaluation, considering the great balance between expected returns and risks. Utilizing D- Wave's hybrid solver, we present a thorough analysis and discussion of the proposed model realized in the QA structure. The experimental results demonstrate that our model can discover solutions of comparable quality in a significantly shorter amount of time than an exhaustive search. When extending the search space to sizes challenging for exhaustive search, we conducted experiments comparing our approach with state-of-the-art quantum-inspired artificial intelligence (AI) algorithms. The results show that our method not only constructs higher-quality solutions but also requires the least computation time. The hybrid quantum-classical AI represents a forward-looking technology paradigm poised to revolutionize problem-solving methods.

Quantum-inspired optimization is an emerging computational intelligence technique that achieves quantum advan-tages through inspiration from the mechanisms of quantum properties. Parameter control is vital for optimization methods to address complex problems with extensive solution spaces. However, static parameters lack adaptability, leading to issues like premature convergence, becoming trapped in local optima, and failing to uncover the global optimum. Therefore, we develop a novel adaptive mechanism to dynamically adjust parameters during algorithm execution to strike a remarkable balance between exploration and exploitation. This study first proposes the knowledge-navigated quantum-inspired tabu search (KNQTS) algorithm, which acquires insights from the ongoing process to adjust the environment-sensitive parameters adaptively and non-linearly. Differences between inter-iteration solutions can signal the extent of convergence and guide bidirectional adjustments of a parameter, facilitating the handling of com-plex optimization problems. KNQTS is applied to tackle real-world application portfolio construction problems in the financial technology field. Comprehensive experiments are conducted to demonstrate the substantial potential and robustness of KNQTS in rapidly identifying better solutions with higher probabilities in shorter durations compared to the other state-of-the-art methods. Statistical tests substantiate the significant superiority of KNQTS over these comparative algorithms.

Quantum-inspired evolutionary algorithms harness quantum properties to optimize the search process within classical computers, efficiently addressing complex and challenging problems. This study first proposes an intrusion detection system (IDS) based on a hybrid model using quantum-inspired evolutionary neural networks. The model integrates a deep neural network (DNN) and a global best-guided quantum-inspired tabu search algorithm (GQTS). To safeguard against potential threats, an IDS is deployed to monitor network or system traffic and detect malicious attacks. Anomaly detection, a pivotal aspect of IDS, aims to establish a normal model to respond effectively to unknown abnormal attacks. The experiment utilizes the latest dataset, CICIDS2017, which is generated based on realistic background traffic. During the training phase, GQTS selects valid features from the dataset and optimizes the hyperparameters of the DNN setting automatically, significantly contributing to improving accuracy and reducing the false negative rate. The results highlight that the proposed hybrid model decreases computational complexity through feature selection and enhances model accuracy via suitable hyperparameter optimization compared to other state-of-the-art methods. The proposed model demonstrates great potential over alternative structures.

Quantum-inspired optimization (QIO) has garnered attention for attempting to retain quantum benefits on classical computers, thereby improving search efficiency in solving complex optimization problems. Portfolio optimization is one of the complicated real-world applications that concerns conflicting objectives of profit and risk simultaneously, making it a biobjective problem. This study exploits the advantage of the QIO to propose a multi-objective quantum-inspired tabu search algorithm (MoQTS) for constructing the Pareto front (PF) for portfolio optimization based on the innovative bi-objective trend ratio model. MoQTS initially employs the superposition encoding mechanism and Q-gate to search for potential areas quickly while maintaining the memory of PF information. Then, the entanglement move expands the search direction along with the current PF with more diversity. This study provides exhaustive search results to examine the completeness of optimal solutions in PF. The experimental results demonstrate that MoQTS exhibits competitive performance compared to classical methods across various metrics, including inverted generational distance (IGD), hypervolume (HV), and others. MoQTS shows significant potential in generating the PF using fewer computational resources.

Financial management is a critical and complicated issue. People tend to invest in the stock market and expect a stable uptrend portfolio to spread the investment risk effectively. As global economies affect each other, investing in global markets is the best way to address systematic risk for effective financial management. Selecting an appropriate portfolio across global markets involves a substantial solution space; thus, an efficient and effective evolutionary computation is proposed. The novel entanglement-enhanced quantum-inspired optimization technique is proposed as an efficient mechanism to search for the relationship between stocks with high-dependency solutions. The novel indicator trend ratio aims to evaluate the investor-desired portfolio with a stable uptrend under a perfect balance between return and risk. Therefore, this study is the first attempt to evaluate the major global markets in the Group of Seven (G7) countries to expand the generality of trend ratio usage. The experiments demonstrate that the proposed novel approach has better and more precise searchability than other state-of-the-art optimization. The proposed intelligent model outperforms the Sharpe ratio, benchmark strategy, and index performance regarding the trend ratio, risk, maximum drawdown, and profit factor. The results reveal that risk dispersion substantially improves investment performance. Under the evaluation of the trend ratio, the proposed system integrates G7 markets as an expansion of a prospective avenue for global asset management,

Computational intelligence (CI) has been extensively used in financial technology areas to help make smart decisions within enormous solution spaces. This study proposes a new portfolio optimization model using hybrid strategies to concurrently consider long and short investments and compose effective portfolios assessed by an emerging indicator to reach an excellent balance between return and risk. It exploits the trend ratio investment strategy to a detailed extent to evaluate the steady uptrend and downtrend portfolios and provides an in-depth discussion of the U.S. stock market. The proposed method combines long and short selling through a single fund to enhance investment efficiency. Quantum-inspired CI is adopted to construct a near-optimal portfolio efficiently and effectively, and sliding windows are applied to rebalance the portfolio and dynamically discover appropriate periods. The experiment conducts comprehensive statistical tests to show that hybrid strategies significantly improve traditional approaches. The comparison with other traditional methods shows outstanding performances in MDD, PF, RoMaD, return, and trend ratio. The proposed hybrid portfolio optimization application can be complementary, offset the volatility and increase the return, and show great potential in the financial industry.

With the growing popularity of online trading platforms, investors can consider investing in the global market without regard to their geographical location. This study analyzes the investment performance of Group of Seven (G7) members, who greatly influence the global economy and stock market. Regardless of the market in which they invest, investors seek long-term and stable gains in the stock market; therefore, investing in a risk diversification option is paramount. By constructing a portfolio, investment risk can be decreased. The relationship between return and risk is crucial. In this study, the proposed intelligent investing system with a novel trend ratio indicator aids in constructing an efficient portfolio. In a volatile stock market, the daily ups and downs are all profit opportunities; therefore, the proposed intelligent system constructs long-selling and short-selling portfolios. This study expands the investment discussion market to the G7 market and combines trading strategies into an innovative hybrid trading strategy to evaluate the G7 market's overall performance. As the trading strategies and markets under consideration are more inclusive, the proposed quantum-inspired algorithm with the entanglement technique can address the vast portfolio construction solution space. This study generalizes trend ratios in long-selling and short-selling portfolios and G7 markets. The experiment's result reveals that the hybrid strategy can increase portfolio profitability and efficiency.

Reversible circuits are the essential building blocks of quantum computers and have zero energy dissipation. However, there is no general rule on how to synthesize an effective circuit with minimal cost. Many researchers have placed a high value on the design of algorithms for reversible circuit synthesis as it is the fundamental component to implement in many paradigms, such as Shor’s algorithm. In this paper, the knowledge navigated quantum-inspired tabu search algorithm (KNQTS) is proposed to synthesize several benchmark functions. KNQTS is a quantum-inspired algorithm with the concept of getting closer to the best solution and keeping away from the worst solution, and it has a great search ability. Furthermore, the proposed algorithm uses self-adaptive, global-best guided and Quantum-Not gate mechanisms to make all procedures more efficient and avoid sticking to the local optimum. This paper also compares the KNQTS approach’s experimental results with those obtained via other metaheuristics and previous algorithms. Finally, the result shows that the proposed KNQTS method outperforms other state-of-the-art methods and achieves the same functionality at a lower cost. The solutions are optimal or near-optimal.

Portfolio optimization is a paramount and important issue in the financial technology area. Constructing a portfolio requires simultaneous considerations of both return and risk, and the proposed system utilizes the trend ratio based on funds standardization, which can effectively evaluate the performance of a portfolio by its returns and risks, while the quantum-inspired tabu search (QTS) algorithm can be used to efficiently build the best portfolio with a stable uptrend. As it is difficult for users to observe a large amount of stock data, this system helps investors to deal with portfolio optimization issues and further provides an information visualization interface to analyze more investment situations. The user interface of the proposed system provides a new service for users to get started quickly in stock selection and clearly analyze the performance of portfolios in the different stock markets. The proposed method can also push notifications automatically when the system detects that the trend of the user’s portfolio is going to slow down, and reminds them to invest in new portfolios with a strong uptrend in a timely manner. The system also designs three games. The first two games make people familiar with stocks. The third game combines social computing with gameplay, which helps to simultaneously find a better portfolio in the highly complex data. In addition, our system provides the functions of general financial applications, such as stock price inquiry, financial news, etc.

When investors invest in the stock market, the first decision is invested in which stock market and how to decide on the invested stock. The high return stock is accompanied by high risk. Constructing a portfolio can help to diversify the risk in the investment. This paper proposes a decision support system that can help select stocks and construct a portfolio with high return and low risk precisely in the Singapore stock market, which is one of the top performances in Asia. The proposed system uses the novel assessment indicator trend ratio to simultaneously consider the portfolio return and risk and then uses the global-best guided quantum-inspired tabu search algorithm with quantum-NOT gate (GNQTS) to search for the near-optimal solution. The decision-making system also includes 13 sliding windows to retain the freshest data and identify the suitable investment period. According to the experimental results, the system consisting of evolutionary computation can perform robust and rational decisions to efficiently construct a stable uptrend portfolio with the highest trend ratio in the Singapore stock market.

Metaheuristics are crucial for solving complex optimization problems effectively in many fields. With the recent increased interest in explainable artificial intelligence (XAI), the issue of how metaheuristics interact with XAI has attracted much attention. Most metaheuristics have stochastic search processes, and the jaguar algorithm (JA) is a unique algorithm that has exact search paths. JA shows potential abilities both in exploration and exploitation, but still faces limitations. Therefore, this study proposes a new metaheuristic based on JA and invents precision hunting behavior (PH-JA), which inherits the concept of JA and significantly strengthens its search efficiency. PH-JA improves the solution quality by adaptively detecting the current environment’s trends during movement and precisely hunting prey. Then, PH-JA makes the best of the historical information to reduce the computational cost. PH-JA is an explainable metaheuristic and has systematic operational procedures in that each movement is performed according to the present situation rather than random elements. Therefore, the path to a solution is interpretable and can return the same result with the same input. The experimental results demonstrated the superiority of PH-JA, which is better than other classical and state-of-the-art metaheuristics in terms of solution quality and evaluation costs.

Investing in stocks is one of the most common options of financial management, and many people use technical indicators to decide when to buy and sell stocks. This research proposes a novel method of utilizing the commonly used technical indicator, Relative Strength Index (RSI), to the extreme. First, we remove the restriction of the traditional RSI’s default parameters and expand all the possibilities in RSI to find the best strategy and to maximize profit. Second, we employ a modified metaheuristic algorithm, the global best-guide quantum-inspired tabu search algorithm with quantum not gate (GNQTS), to efficiently search for the optimized parameters for RSI. Third, our approach also applies a sliding window to flexibly adjust training periods and avoid over fitting at the same time. The experimental environment covers popular indices and companies in the United States stock market such as DJIA, AAPL, etc. By removing the restriction of RSI, the experiment result shows that GNQTS can find optimized parameters of RSI to get more profit than traditional RSI and buy-and-hold strategies.

Metaheuristic algorithms play an extremely important role in the computational intelligence and optimization fields. The jaguar algorithm (JA) is a metaheuristic algorithm that has outstanding performances in both exploitation and exploration. This study proposed a novel metaheuristic algorithm, named the fast jaguar algorithm (FJA), which inherits JA’s advantage and significantly enhances its search ability. FJA uses the fast hunting mechanism to identify the trend in the local area effectively, and it only searches for the better side to reduce the computational cost. FJA well uses historical information through the adaptive exploit mechanism, which allows it to efficiently find the better tendency. Then, FJA utilizes the jump mechanism to discover the global optimal solution. FJA comprehensively considers more situations and then makes the right decision to find the optimal solution more precisely. The experiment results demonstrated the robust performance of FJA through function optimization and showed that the efficiency of FJA could outperform JA and other classical metaheuristics in terms of computational cost and stability.

Evolutionary algorithms or metaheuristic methods are common approaches applied in highly complex optimization problems, such as stock trading. In this paper we employ a novel approach for using a dynamic trading system with a technical indicator called the moving average (MA). Moreover, we utilize an improved metaheuristic algorithm, the global-best guided quantum-inspired tabu search algorithm (GQTS), which has a fast and stable feature to efficiently search for the optimal trading strategy of MA. Our approach employs the sliding window technique to avoid overfitting problem. In addition, we propose year-on-year sliding windows and 2-phase sliding window to adapt to the phenomenon of an economic cycle in the investment period and to efficiently solve stock trading problems. The experimental environment is the United States stock market. The experiment result shows that combine GQTS and MA can find better strategies that outperform other normal strategies. The performance of remuneration indicates that the trading system is enhanced with a year-on-year sliding window and 2-phase sliding window.

Selecting a portfolio with high return and low risk is a difficult problem that is worthy of research. When solving a portfolio optimization problem, research studies usually employ various assessing indicators. This paper utilizes the trend ratio as the measure indicator. The value of the trend ratio represents how much the return is from the portfolio per unit of risk. A portfolio selected by the trend ratio based on simple linear regression is more in accordance with investors' psychology. This paper proposes a new investment strategy with fund allocation to solve a portfolio optimization problem, as fund allocation can make the portfolio more flexible and also can reduce risk effectively. This paper uses the Global-best Guided Quantum-inspired Tabu Search with Quantum-NOT Gate (GNQTS) to find how many funds are allocated to each stock and then uses the trend ratio to evaluate the portfolio. Because overfitting is a common problem in the stock market, this paper uses thirteen types of sliding windows to avoid overfitting. The results show that fund allocation is more flexible than allocating equal funds, thus allowing the proposed method to find a portfolio with a higher return and lower risk.

Selecting the best combination of stocks with low risk and high return simultaneously is a significant challenge for investors. The Sharpe ratio is a common way to evaluate the performance of portfolios. However, the Sharp ratio has some defects such as that portfolios in uptrend will be calculated as high risk. Therefore, trend ratio on linear regression is proposed to find the portfolio that has a stable uptrend. Since the linear regression just roughly represents the portfolio trend and cannot be approached precisely, this paper proposes the quadratic regression trend to define the portfolio trend more accurately. In addition, the trend ratio on quadratic regression can signify the change of magnitude slowing down or speeding up in an uptrend or downtrend direction to help this model predict the future trend more precisely. This paper uses the adaptive quantum-inspired tabu search with not gate (ANQTS) to search the best portfolio and employs the 2-phase sliding window to consider more investment situations. The experiment results show that the proposed portfolio optimization model has better performance than the Sharp ratio and trend ratio on linear regression.

When investing in the stock market, investors first encounter the stock selection problem. Therefore, how to select a potential combination of stocks is a problem worth investigate. One commonly-used risk indicator is the Sharpe ratio. However, it defies the logic of investors because even an uptrend portfolio has a high risk. Thus, this paper proposes a strategy to improve the Sharpe ration denoted the trend ratio where the daily expected return is the slope of the trend line and the risk is the difference between the trend line and the fund standardization. Moreover, we propose doing normal trading and short selling simultaneously to increase the profit and spread the risk. We use the trend ratio to find a stable uptrend portfolio for normal trading and a stable downtrend portfolio for short selling. As there is no limitation to the amount of stocks in a portfolio, and because MPT's computation complexity grows exponentially when the number of stocks increases, we utilize the Quantum-inspired Tabu Search algorithm improved by the quantum-not-gate (GNQTS), to find an optimal portfolio in a large solution space. Besides, we use the sliding window to overcome the over-fitting problem. In addition of using the nearest time period as the training period, we use the same time period from the last year as the training period and tested it in the same period in current year, as some stocks have an economic cycle. Using the trend ratio while doing normal trading and short selling, with the GNQTS and sliding window, the experiment results show a promising result in which the risk is spread effectively and the profit is maximized.

In the stock selection problem, the Sharpe ratio is one of the commonly used indicators, but it tends to identify the portfolio with a flat trend as the best one. This paper uses the trend ratio to access the portfolio with a stable upward trend. By the portfolio trend line with initial funds, the trend ratio can simultaneously consider the daily expected return, daily risk and fairly compare with the different portfolios and different investment periods lengths. In addition to the access indicator, this paper provides diversified investments such as time deposit, buying round lots or buying odd lots. Different situation suits different investment method. Therefore, this paper applies the 2-phase investment sliding windows to avoid the overfitting problem and chooses the best investment method by multiple training using Global-best Guided Quantum-inspired Tabu Search with Not Gate (GNQTS) to find the best portfolio effectively and efficiently. Furthermore, the experimental results show that the proposed method can find the well-performing portfolio with higher return and lower risk in both the training and testing periods.

Jaguar algorithm (JA) has excellent performance in solving optimization problems. Different from traditional metaheuristic algorithms, JA has great abilities both in exploitation and exploration. Therefore, JA can find the best solution more quickly and efficiently. However, JA has some defects. JA considers only one dimension at a time, therefore it requires more evaluations. Moreover, JA does not adjust its step according to the distance of prey. To solve these problems, this study proposes the multi-dimensional jaguar algorithm (MJA) can consider multiple dimensions simultaneously and hunt all preys at the same time. MJA includes the adaptive step, which is adjusted according to the level of the solution. In this way, MJA can adapt its step in every dimension and can rush to its prey more accurately, which allows it to find the local optimal solution more efficiently than traditional JA. Furthermore, MJA uses information from multiple territories to jump diagonally to find the global optimum more accurately and efficiently. MJA, therefore, has stronger search ability than traditional JA. The self-analysis and experiments of this study show that the performance of MJA is better than traditional JA and other metaheuristic algorithms.

Metaheuristic algorithms are used to solve complex optimization problems, including numerical optimization problems and combinatorial optimization problems. Currently, many applications exist to help users learn how algorithms work on numerical optimization problems, however, there are no applications offering the same tool for combinatorial optimization problems. Combinatorial optimization problems usually have high dimensionality making them very difficult to analyze. Therefore, this study proposes a novel and useful tool, EPanel 2.0, to display the process of metaheuristic algorithms solving not only numerical optimization problems but also combinatorial optimization problems. EPanel 2.0 is implemented in JAVA, making it executable in multiple operating systems. EPanel 2.0 has a clear user interface, provides an animated representation of the process, and also showing solutions in different ways on different situation. EPanel 2.0 allows users to easily analyze each step of the algorithm. In conclusion, EPanel 2.0 is a useful application help researcher solving complex optimization problems.

This research proposes a novel dynamic trading system utilizing a simple but common technical indicator, the moving average (MA). We also analyze the weight moving average (WMA) and exponential moving average (EMA), which has the multiplying factor of MA. In addition, a modified evolutionary algorithm, the globe best-guide quantum-inspired tabu search algorithm (GQTS), was invented to quickly and stably search for the optimal combination of MA parameters. In order to avoid the overfitting problem, this approach applied the 2-phase sliding window and year-onyear training period to address more comprehensive stock trading problems. In addition to normal stock trading, this system adopts another legal trading method, short selling. The experiment results reveal that our method has a greatly improved MA ability and the WMA has the best performance in four different targets. The sliding window period with 2-phase and year-on-year can improve the performance of the trading system. When the trading system adopts short selling it can significantly improve investment profit.

Metaheuristic algorithms are important methods to solve many complicated optimization problems. However, there are no significant studies that present an application to analyze metaheuristic algorithms or a platform to help beginners to learn about. For this reason, we design a useful tool with a graphical user interface, EPanel, to dynamically display the process of metaheuristic algorithms. EPanel displays the value of a function in one or two dimensions by using different colors. It helps users understand the distribution of the solution space, such as unimodal, multimodal, and narrow valley. EPanel then shows the position of every particle above the screen of a function in each iteration and displays the complete optimization process of the algorithm by animation. Implemented with JAVA, EPanel is able to be executed in different operating systems. EPanel helps researchers to analyze each step of the algorithm and to observe how the algorithm achieves exploratory and exploitation. By using EPanel, research can further develop or improve algorithms efficiently. Beginners can learn about metaheuristic algorithms through EPanel. They can see popular algorithms, such as Particle Swarm Optimization and Artificial Bee Colony, work in each movement. EPanel is a useful application and helps not only researchers but also beginners.

Stock price forecasting is an important issue for investors since extreme accuracy in forecasting can bring about high profits. Fuzzy Time Series (FTS) and Longest Common/Repeated Sub-sequence (LCS/LRS) are two important issues for forecasting prices. However, to the best of our knowledge, there are no significant studies using LCS/LRS to predict stock prices. It is impossible that prices stay exactly the same as historic prices. Therefore, this paper proposes a state-of-the-art method which combines FTS and LCS/LRS to predict stock prices. This method is based on the principle that history will repeat itself. It uses different interval lengths in FTS to fuzzify the prices, and LCS/LRS to look for the same pattern in the historical prices to predict future stock prics. In the experiment, we examine various intervals of fuzzy time sets in order to achieve high prediction accuracy. The proposed method outperforms traditional methods in terms of prediction accuracy and, furthermore, it is easy to implement.

Stock price predicting is an important concern for investors, who by using high accuracy prediction systems are able to make a great profit. In recent years, artificial neural networks (ANNs) have shown promising results in this area, and have been improved in many ways. However, there are still some issues with ANN that remain unanswered, one of which is how to set the best parameters for ANN. Different combinations of setting parameters bring about different consequents, such as the constitution of input nodes, hidden nodes, and initial values of weight. Hence, we propose a simple but useful method, which only uses stock closing prices as inputs and experiments with different kinds of setting parameters. In addition, this paper enhances back propagation neural network (BPN) with a novel normalized function. System is applied to Taiwans Top 50 Exchange Traded Fund, S&P 500, and Shenzhen Composite to forecast the next day closing price. Given the experimental results, the proposed method shows excellent performance with the best set of parameters, and the innovative normalization method effectively improves accuracy. Moreover, our system provides better results in terms of accuracy of prediction than other systems. Finally, this study provides a method for how to design setting parameters in BPN.

The most powerful metaheuristics must be good at both exploitation and exploration. In the present day, metaheuristics are designed to reach a balance between these two capabilities for the sake of avoiding being trapped in the local optimum or unable to achieve convergence. For the first time in history, it is noteworthy that exploitation and exploration are both strong in the use of the Jaguar Algorithm (JA). In this paper, JA presents a simple but robust method inspired by the behaviors of jaguars. One feature of the jaguar is that once a jaguar is locked onto its prey, the jaguar moves directly and swiftly toward the target in the hunting area that has been established as its own territory. In addition, jaguars can hunt more efficiently when they take advantage of teamwork. This jaguar behavior parallels the behavior that makes JA more efficient than other well-known algorithms in exploiting and exploring. The experiment of this research reveals that an appropriate cooperation of jaguars could have various positive influences in regard to benchmark functions.

Wireless sensor networks (WSN) can be widely used in many areas, such as environment monitoring, weather forecasting, traffic control, etc. The wormhole attack problem is an important issue in WSN since it causes many problems, such as routing error, a reduction in sensor lifetime and broken network topology. Several wormhole detection approaches have been proposed, but most of them need special hardware devices and consume a lot of system resources. This paper proposes a novel and efficient method to detect the wormhole attack without hardware equipment or requiring much information about WSN. The proposed method uses a moving average (MA) indicator, which has been commonly used in financial fields, to apply to neighbors of sensor nodes, it becomes a dynamic detection indicator of the number of neighbor nodes. Because the combinations are too numerous to arrange, we utilize a Quantum-inspired Tabu Search (QTS) algorithm. This algorithm is efficient and effective in finding the ideal combination of detection indicators to detect wormhole attacks in different scenarios. The simulation result shows our method is intuitive and efficiently detects wormhole.

Stock selection is an important issue when it comes to investing in the stock market. However, it is worth investigating the problem of selecting portfolios while considering not only low risk but also high return on investment. The calculation process of the traditional method is highly complex and is not comprehensive in terms of what it takes into consideration. Hence, this paper proposes a new method to calculate portfolio risk. We utilize funds standardization in order to consider the risk of a portfolio and drastically reduce computation complexity. Funds standardization is able to represent fluctuations of investor mood. Moreover, using a Genetic algorithm (GA) combined with the Sharpe Ratio is able to identify the low risk and stable returns of a portfolio. Moreover, over-fitting is a common problem in the stock market, and so this paper uses sliding windows to avoid the over-fitting problem, and tests all kinds of training periods and testing periods that impact on the portfolio. The experimental results show that the proposed method, compared with the traditional method of calculating risk, is able to identify the optimal portfolio and performs efficiently and outstandingly when it comes to this problem.

Reversible circuit synthesis is an important field in quantum computing, low-power design, and reversible circuit. The problem of reversible circuit synthesis is complicated and hard to solve because the input of the reversible circuit will experience a huge increase when extended to multi-bit. In this paper, a novel algorithm of reversible circuit synthesis is proposed. With the satisfied priority of positions, we can efficiently select the position which should be satisfied, and each selected gate is generated to benefit the overall situation. Therefore, the proposed algorithm is able to optimize the reversible circuit with fewer gates and can be implemented in regard to multibit reversible circuit synthesis. In addition, the gate set we used contains only Toffoli, spanning all wires. This Toffoli gate is liable to be transformed into another gate set and is helpful for algorithm design. The experiment shows that the proposed algorithm, with efficient and optimal results, is superior to others.

In quantum computing, the synthesis of reversible circuits is an important topic. Reversible circuit synthesis is particularly challenging because the complexity grows as the number of bits increases. To date, many reversible circuit synthesis algorithms have been proposed, but most are unable to find the optima within an acceptable time. Because traditional methods only consider partial interests, the resulting cost would be more gates. This paper proposes a novel method, called Bound Oriented Algorithm, which has the ability to find the optimal solution with a high hit rate, one greater than 75% on average. Moreover, with the prediction of the optima by bound, it can reduce excess calculation to further improve efficiency. In addition, a special library containing only Toffoli gates is used, which simplifies algorithm design and is more easily converted to a common library. The experiment result shows that the proposed method performs better than other methods in terms of solution quality and time cost.

Quantum machines are powerful computation machines capable of parallel computation. A great deal of research has therefore focused on algorithms based on the properties of quantum physics, including superposition and entanglement. However, these techniques face significant difficulties when dealing with reversible circuit synthesis, in which the functions, computation and storage increase by factorial as the number of input bits is increased. The most efficient methods of dealing with reversible circuit synthesis are currently limited to 5 bit inputs. However, this research proposes a novel concept which reduces the number of input functions by classification. It is based on the relationship between a hypercube and gates. The algorithm is able to classify functions with the same properties into an isomorphic class, and is able to do this for any input bits. This not only efficiently reduces the output functions, but also speeds up the synthesis algorithm.

Recently evolutionary algorithms, such as the Genetic Algorithm (GA), Genetic Programming (GP) and Particle Swarm Optimization (PSO), have become common approaches used in financial applications to address stock trading problems. In this paper, we propose a novel method called the Multi-objective Quantum-inspired Tabu Search (MOQTS) algorithm, which can be applied in a stock trading system. Determining the best time to buy and sell in the stock market and maximizing profits while incurring fewer risks are important issues in financial research. In order to identify ideal trading points, the proposed trading system uses various kinds of technical indicators as trading rules in order to cope with different stock situations. The proposed algorithm is used to identify the optimal combination of trading rules as our trading strategy. Moreover, it makes use of a sliding window in order to avoid the major problem of over-fitting. In the experiment, the algorithm uses both profit earned and other aspects, such as successful transaction rate and standard deviation, to analyze this system. The experimental results, in relation to profit earned and successful transaction rates in the U.S.A stock market, outperform both the traditional method and the Buy & Hold method which are common benchmarks in the field.

Many heuristic methods or evolutionary algorithms such as Genetic Algorithm (GA) and Genetic Programming (GP) are common approaches used in financial applications. Determining the best time to buy and sell in a stock market, and thereby maximizing the profit with lower risks are important issues in financial research. Recent researches have used trading rules based on technical analysis to address this problem. These rules can determine trading times by analyzing the value of technical indicators. In other words, we can make trading rules by analyzing the value of technical indicators. A simple example of a trading rule would be, if one technical indicator's value achieves the pre-defined value, then we can buy or sell stocks. A combination of trading rules would become a trading strategy. The process of making trading strategies can be formulated as a combinatorial optimization problem. In this paper, a novel method which can be applied to a trading system is proposed. First, the proposed system uses the Quantum-inspired Tabu Search (QTS) algorithm to find the optimal combination of trading rules. Second, it uses sliding window to avoid the major problem of over-fitting. The experiment results of earning profit show much better performance than other approaches. Especially, the proposed method outperforms Buy & Hold method which is a common benchmark in this field.

In this paper, we propose a novel method named Quantum-inspired Tabu Search (QTS) algorithm for applying to a trading system. Determining the best time to buy and sell in a stock market and thereby maximizing the profit with lower risks are important issues in financial research. In order to find ideal trading points, the proposed trading system use technical indicators as the composition of trading rules. Also, it makes use of sliding window to avoid the major problem of over-fitting. The experiment results of earning profit in Japan stock market outperform Buy & Hold method which is a common benchmark in this field. Especially, the proposed method also shows better performance than other approach.

After we read the paper about quantum-inspired tabu search algorithm (QTS) for solving 0/1 knapsack problems [5], we got many ideas. In this study, we proposed a method which is called improved quantum-inspired tabu search algorithm (IMQTS). In IMQTS, we add two skills in QTS. First, we add the probability of taking a worse solution become the guide of updating the populations. Second, we add a second rotation which is turning possible solutions away from the worst solution. We use IMQTS for solving function optimization problem to show its performance. The experiment results show that IMQTS performs well in function optimization problem, and IMQTS would not fall into local optimum.

Reversible logic plays an important role in quantum computation, which is a promising research field. The reversible logic synthesis problem focuses on generating a reversible circuit automatically and finding the lowest cost when an output function is given. The synthesis of reversible logic circuits can be formulated as a combinatorial optimization problem. This paper proposes a new evolutionary algorithm for synthesizing reversible circuits based on Quantum-Inspired Tabu Search Algorithm (QTS). The proposed algorithm uses the QTS-based approach to find fewer gates and reduce the cost of reversible circuits. This method is simpler, has better performance in computational cost, and reduce the gate counts of reversible circuits. This paper also compares experimental results with other heuristic and evolutionary algorithms. The final outcome shows that the QTS-based approach performs much better than other algorithms.