In the past decades, various attention has been paid to the global optimization problems. The Monarch Butterfly Optimization (MBO) algorithm is an effective meta-heuristic algorithm for the global optimization problems. However, in the MBO, the diversity of the population is lost in the late iteration. The MBO is easy to trap into the local optima. In this study, an algorithm based on MBO with learning mechanism and topological structure, named LTMBO, is proposed to enhance the ability of exploration and exploitation on the global optimization problems. The learning mechanism is present for the migration operator to increase the speed of the iteration. The topological structure is proposed for the butterfly adjusting operator to improve the diversity of the population. The experimental results demonstrated that the efficiency and significance of the proposed LTMBO algorithm.

A new metaheuristic algorithm, named surrogate-guided algorithm(S-Jaya), is proposed to solve the single objective continuous optimization problems in this paper. A novel mutation strategy for the non-separable single objective continuous optimization problems is introduced to alter the search engine of the Jaya algorithm. The surrogate is embedded to accelerate the convergence of the population and avoid the proposed algorithm falling into the local optimal during the evolutionary process. The suggested S-Jaya algorithm to address the CEC 2017 benchmark problems is effective and validated. On the quality of solution and execution time, the experimental results reveal that the effectiveness of the S-Jaya algorithm is superior compare with the Jaya algorithm and its variants.

Backtracking search algorithm (BSA) has been applied to solve the various optimization problems in recent years. However, BSA is difficult to solve non-separable problems due to its single search mechanism. In this paper, backtracking search algorithm based on knowledge of different populations, named DKBSA, is proposed to solve continuous optimization problems. In DKBSA, sub-population partitioning method is used to enhance the local search ability and alleviate the loss rate of the diversity of population. Afterwards, a mutation strategy with knowledge guidance and rotation invariance, which is based on the current sub-population information and historical information, is designed to improve the convergence speed of the DKBSA. Furthermore, a control parameter of adaptive search factor is embedded in the mutation strategy to balance the exploitation and exploration of the proposed algorithm. Finally, a probabilistic model-based strategy is proposed to generate dominant individuals to further improve the search ability of the proposed algorithm. The experimental results of the state-of-the-art algorithms in the CEC2017 benchmark test suit reveal that the DKBSA is effective for solving non-separable problems.

The invasive weed algorithm (IWO) is a meta-heuristic algorithm, which is an effective and promising optimiser to address the optimisation problems. In this study, a hybrid algorithm based on the self-adaptive invasive weed algorithm (IWO) and differential evolution algorithm (DE), named SIWODE, is proposed to address the continuous optimisation problems. In the proposed SIWODE, first, the two parameters are adaptively proposed to improve the convergence speed of the algorithm. Second, the crossover and mutation operations are introduced in SIWODE to improve the population diversity and increase the exploration capability during the iterative process. Furthermore, a local perturbation strategy is presented to improve exploitation ability during the late process. The exploration and exploitation ability of the algorithm is effectively balanced by cooperative mechanisms. The experiment results of SIWODE show that the SIWODE has the superior searching quality and stability than other mentioned approaches.

Water Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed. First, the random opposition-based learning (ROBL) mechanism is introduced to generate the initial population with high quality. Second, a new modified operation is designed and embedded into propagation operation to balance the global exploration and the local exploitation. Third, the covariance matrix self-adaptation evolution strategy (CMA-ES) is employed by the refraction operation to further strengthen the local exploitation. Furthermore, the diversity of the population is maintained in the evolution process by using a crossover operator. The experiment results based on CEC 2017 benchmarks indicate that the EWWO outperforms the state-of-the-art variant algorithms of the WWO and the standard WWO.

In order to improve the accuracy of human action recognition in video and the computational efficiency of large data sets, an action recognition algorithm based on multiple features and modified deep learning model is proposed. First, the deep network pre-training process is used to learn and optimize the RBM parameters, and the deep belief nets (DBN) model is constructed through deep learning. Then, human 13 joint points and critical points of optical flow are automatically extracted by DBN model. Second, these more abstract and more effective human motion features are combined to represent human actions. Ultimately, the entire DBN network structure is fine-tuned by support vector machine (SVM) algorithm to classify human actions. We demonstrate that human 13 joint points and critical points of optical flow are two very effective human action characterizations, our proposed approach greatly reduces the required samples, and shortens the training time of the samples, can efficiently process large data sets and can effectively recognize novel actions. We performed experiments on the KTH data set, Weizmann data set, the ballet data set and UCF101 data set to evaluate the proposed method, the experiment results show that the average recognition accuracy is over 98%, which validates its effectiveness, and show that our results are stable, reliable, and significantly better than the results of two state-of-the-art approaches on four different data sets. So, it lays a good theoretical foundation for practical applications.

Multi-objective evolutionary algorithms usually utilize fixed evolutionary mechanism and the evolutionary operators are static during the process of algorithm evolution. It is easy to cause a simple population structure, unable to exploit the search space fully and trapped in local optimal solution. In this paper, a novel method named Pareto Archive Evolution Strategy (PAES) with adaptive grid strategy (AGS_PAES) which only makes one mutation to create one new solution and use an “archive” which are called Non-Dominated Archive to store the best solution, is introduced. This procedure is completed by a special approach - adaptive grid method, which decides the criterion of the solution to be archived and the place of the grid location the solution would be stored. The Pareto front obtained by the procedure outperforms the classical Multi-objective Genetic Algorithm (MOGA). Simulation results on the standard benchmark problems show that the proposed adaptive scheme has a better convergence and diversity compared with the second generation classical multi-objective evolutionary algorithms.

In the production system, the machine processing ability is unequal, which leads to the low utilization rate of the machine and the long waiting time of the workpiece. Aiming at these problems, the Markov queuing model with unequal processing capability is proposed. A dynamic optimization method of integrated workshop performance indexes based on unequal processing capacities is studied. The M/M/2 and M/M/3 queuing models with Unequal processing abilities are simulated by using the signal simulation module in Matlab. The queuing rules in Markov's queuing model with Unequal processing abilities are optimized aiming at the unequal processing capacities, and a comprehensive priority queuing model is also proposed. Using the same queue rule will reduce the production efficiency of production system under different production intensity, so we propose integrated priority queuing model.

NSGA-II is a well known, fast sorting and elite multi-objective genetic algorithm. The local exploitation ability of NSGA-II is relatively limited by the parameters of crossover and mutation. DE has shown powerful search abilities for continuous optimization. In this paper, an enhanced NSGA-II based on differential evolution and L-near distance (DP-NSGA-II/EDA) is proposed. To improve the diversity and convergence of Pareto optimal solutions by NSGA-II algorithm, DP-NSGA-II/EDA produces two populations by different approaches. One is from NSGA-II itself, the other is from differential evolution (DE). Through the competition between two populations, the superior individuals will be selected to construct new offspring population. Meanwhile, a new distance strategy called L-near distance is introduced to NSGA-II to maintain the diversity of the population. To validate the proposed algorithm, it is compared with the original NSGA-II, SPEA2 and MOEA/D-DE through several numerical benchmark problems. Results show the effectiveness of the proposed approach.

Abstract Probabilistic approaches to rough sets are still an important issue in rough set theory. Although many studies have been written on this topic, they focus on approximating a crisp concept in the universe of discourse, with less effort on approximating a fuzzy concept in the universe of discourse. This article investigates the rough approximation of a fuzzy concept on a probabilistic approximation space over two universes. We first present the definition of a lower and upper approximation of a fuzzy set with respect to a probabilistic approximation space over two universes by defining the conditional probability of a fuzzy event. That is, we define the rough fuzzy set on a probabilistic approximation space over two universes. We then define the fuzzy probabilistic approximation over two universes by introducing a probability measure to the approximation space over two universes. Then, we establish the fuzzy rough set model on the probabilistic approximation space over two universes. Meanwhile, we study some properties of both rough fuzzy sets and fuzzy rough sets on the probabilistic approximation space over two universes. Also, we compare the proposed model with the existing models to show the superiority of the model given in this paper. Furthermore, we apply the fuzzy rough set on the probabilistic approximation over two universes to emergency decision‐making in unconventional emergency management. We establish an approach to online emergency decision‐making by using the fuzzy rough set model on the probabilistic approximation over two universes. Finally, we apply our approach to a numerical example of emergency decision‐making in order to illustrate the validity of the proposed method.

Abstract This paper proposes a new generalization of classical real‐valued information systems, that is, interval‐valued information systems. By defining an interval‐valued dominance relation on a condition attribute, a rough set model and attribute reduction are established over interval‐valued information systems. Moreover, several interesting properties are investigated by constructive approach. Furthermore, knowledge reductions of consistent and inconsistent interval‐valued dominance decision information systems are studied, respectively. Subsequently, some descriptive theorems of knowledge reduction are presented for interval‐valued dominance decision information systems. Finally, the validity of the model and conclusions is verified by numerical example.

Many automatic or semi-automatic extraction techniques have been proposed for building domain ontologies in recent years but the correctness, consistency and completeness of the extracted ontologies is often either not considered or is not formally verified. The issue of detecting potential anomalies in an ontology has not to date been adequately addressed. In this paper we propose a formal technique for ontology representation and inference, based on which an automatic technique for ontology verification can be developed so as to be able to detect and identify potential anomalies in an ontology. The technique makes use of a State Controlled Coloured Petri Net (SCCPN), which is a high level net that combines a Coloured Petri Net and a State Controlled Petri Net. This work presents a formal definition of SCCPN for modeling ontologies and the mapping between them as well as formulating the ontology inference in SCCPN with specified inference mechanisms.

In real projects, both the trade-off between the project cost and the project completion time, and the uncertainty of the environment are considerable aspects for decision-makers. However, the research on the time-cost tradeoff problem seldom concerns fuzzy environments. In this paper, a new fuzzy time-cost trade-off model with the philosophy of dependent-chance programming is proposed, in which credibility theory is applied to describe the uncertainty of activity durations. A searching method as a hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is produced to search the optimal schedule under the given decision-making rule. The purpose of the paper is to reveal how to obtain the optimal balance of the project completion time and the project cost in fuzzy environment.

Combining with the characteristic of hazardous waste, this paper develops a multi-objective mathematic model for the location of treatment sites and transfer sites for hazardous wastes. Based on the fuzzy satisfactory levels of objectives, it proposes a two-phase fuzzy algorithm. Through solving the model, it conducts an analysis on the locations and numbers of these sites and how to assign the generation sites to transfer sites. Therefore, a reverse network for hazardous waste is constructed Finally, it takes Tianjin Economic-technological Develop Area (TEDA) in Tianjin city in China as a case to prove the availability of the fuzzy model.

Based on some results of the fuzzy network computation and competitive analysis, the Online Fuzzy Most Reliable Path Problem (OFRP), which is one of the most important problems in network optimization with uncertainty, has been originally proposed by our team. In this paper, the preliminaries about fuzzy and the most reliable path and competitive analysis are given first. Following that, the mathematical model of OFRP, in which two kinds of uncertainties, namely online and fuzzy, are combined to be considered at the same time, is established. Then some online fuzzy algorithms are developed to address the OFRP and the rigorous proofs of the competitive analysis are given in detail. Finally, some possible research directions of the OFRP are discussed and the conclusions are drawn.

The proposal of CAT (component aware techniques) is effectively using components that meet the stakeholders' needs for a component based application (CBA). Matching off-the-shelf (OTS) components, using a representation of OTS components as an aggregate of their functional and non-functional requirements and architecture is an important activity. This paper explores the relationship between matching effectiveness with the richness of OTS component structure illustrated using a home appliance control system (HACS) example. Intuition tells that the richer the structure of OTS component, the more effective the matching is. This paper shows positive relationship between OTS model richness and matching effectiveness with experimental study.