Ore-flow blending in open pit mining is a complex real-time scheduling decision problem constrained by high dimensional spatiotemporal dynamics and stochastic uncertainties. Traditional methods often struggle to reconcile the need for long term global stability with the requirement for sub second response times to dynamic events. To address this, we propose a heterogeneous ant colony optimization framework with semantic manifold learning. The framework bridges the gap between static offline learning and dynamic online adaptation. In the offline phase, we introduce a semantic manifold learning mechanism combining genetic programming with quality-diversity optimization. By constructing a behaviorally diverse archive of elite heuristics, this method provides the online system with a "warm start" and diverse initial perspectives, significantly enhancing real-time responsiveness. In the online phase, a heterogeneous ant colony system is deployed where different ant sub populations are coupled with distinct elite rules from the offline archive. This architecture enables parallel exploration of the solution space from multiple semantic perspectives, granting the system intrinsic robustness and high adaptability. Extensive experiments on real-world mine demonstrate that this method significantly outperforms state-of-the-art techniques in ore flow stability control and exhibits superior robustness and scalability across varying road network topologies, equipment configurations, and dynamic scenarios.

The presence of constraints often leads to the formation of narrow and fragmented feasible regions within the search region, presenting significant challenges for optimization problem-solving. This paper introduces a novel approach, Feasible Regions Identification based on Historical Solutions (FRIHS), designed to address these challenges. FRIHS leverages previously evaluated solutions to partition the search region into ε-feasible and ε-infeasible regions. Additionally, by analyzing the correlations among constraints, they are reformulated as auxiliary objectives, effectively transforming the constrained optimization problem into a constrained multi-objective optimization problem. The method employs the classical evolutionary algorithm Differential Evolution and the multi-objective method NSGA-III to search the most promising feasible regions. The effectiveness of FRIHS is evaluated through a comparative analysis with five advanced constraint-handling algorithms across a benchmark test suite. Experimental results indicate that the proposed approach demonstrates competitive performance on the test problems.

In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonies, which is a critical factor influencing algorithm performance, are investigated. Using the dynamic traveling salesman problem as a foundation, we generate test cases to reflect real-world complexities. Our results on a set of problem instances reveal that the choice of communication strategy between colonies significantly impacts the adaptability and efficiency of multi-colony ACO algorithms in tracking moving optimum.

Oil spills are a major source of marine pollution affecting the environment, economy, and marine ecosystems. Toxic chemicals from oil spills can remain in the ocean for years and even sink to the seabed, affecting sedimentation rates. Although many oil spills are caused by accident, some are caused intentionally by cargo ships dumping waste oil and bilge water. It is very difficult to locate, detect and remove oil from the ocean surface. However, regular monitoring can help prevent illegal dumping and aid remediation efforts. This work aims to detect oil spills in the North-Eastern part of Cyprus using a deep learning model. The results are compared with a conventional Adaptive Thresholding Algorithm. The comparisons demonstrate that the deep learning model has higher accuracy than the adaptive thresholding algorithm.

Soil organic matter (SOM) is an important component that exists in soils because it is closely related to soil health and fertility. Hence, knowing the existence of SOM in soils is crucial for management corrections. So far laboratory analysis is required for SOM determination. However, such procedures are costly and labor-time consuming. Alternative methodologies for SOM determination are needed to achieve sustainability. The rise of artificial intelligence and machine learning provide promising approaches that can be exploited for this purpose. The aim of this study is to identify the best regression algorithm for SOM prediction for citrus planted soils. Several machine learning approaches are investigated, including adaptive boosting, gradient boosting, random forest, and multi-layer perceptron neural network.

The rapid growth of Earth Observation (EO) and Remote Sensing (RS) data has underscored the critical need for identifying optimal solutions to effectively manage EO Big Data. This entails simplifying data sharing and facilitating adaptation across multidisciplinary applications to better serve the research community. Various architectures and structures have been developed to manage and deploy these data in an analysis-ready format. In this study, we provide a concise overview of an advanced EO Big Data infrastructure located in Limassol, Cyprus, comprising diverse data sources acquired from an acquisition station, an atmospheric ground base station, and various living labs. Additionally, we present the EO data ecosystem of Cyprus that is specifically designed to efficiently store the aforementioned data.

Monitoring groundwater quality is a costly and time-consuming process. The use of machine learning models has proven to be a suitable alternative for predicting groundwater quality indicators. In this work, an artificial neural network model has been trained via particle swarm optimization (PSO) with hydrochemical data collected from a coastal aquifer in Tunisia. The validity of the PSO-trained model is evaluated based on different performance indicators, demonstrating higher accuracy than the model trained with the traditional gradient descent method concerning all the evaluation metrics. These results are consistent with the well-known ability of these methods to perform more effective searching in the solution space.

Electric vehicle routing problems are challenging variations of the traditional vehicle routing problem which incorporate the possibility of electric vehicle (EV) recharging at any station, while satisfying the delivery demands of customers. This work addresses the recently formulated capacitated vehicle routing problem (E-CVRP) with variable energy consumption rate. In particular, the cargo weight, which is one of the main factors affecting the energy consumption rate of EVs, is considered (i.e., the heavier the EV the higher the rate). As a solution method, an ant colony optimization algorithm with a local search heuristic is developed. Experiments are conducted on a recently generated benchmark set of E-CVRP instances demonstrating that the performance of the proposed technique improves on the best known so far solutions.

In this work, the travel path of a set of drones is scheduled across a graph, where the nodes need to be visited multiple times at pre-defined points in time. The nodes can either be demand nodes requesting monitoring, or supply nodes that are used as take-off/landing locations for the drones and for battery replacement to cope with the limited flying range of the drones. This is an extension of the well-known multiple traveling salesman problem and the proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, the monitoring of remote locations to assist search and rescue missions, or the monitoring of critical infrastructure facilities for security and surveillance purposes. Aiming to find the optimal schedule, the problem is initially formulated as an Integer Linear Program (ILP). However, given that the problem is highly combinatorial, the optimal solution scales only for small-size problems. Thus, a greedy algorithm is also proposed that uses a one-step look-ahead heuristic search mechanism, as well as an algorithm that is based on ant colony optimization (ACO). In a detailed evaluation, it is observed that both algorithms achieve near-optimal performance for small settings, while also scaling to larger settings, with the ACO being more suitable for medium-size settings and the Greedy for larger ones. A field experiment is additionally performed to demonstrate the practical implementation of the proposed system under real-world conditions.

Severa1 logistic companies started utilizing electric vehicles (EVs) in their daily operations to reduce greenhouse gas pollution. However, the limited driving range of EVs may require visits to recharging stations during their operation. These potential visits have to be addressed, avoiding unnecessary long detours. We formulate the electric capacitated vehicle routing problem (E-CVRP), which incorporates the possibility of EVs visiting a recharging station while satisfying the delivery demands of customers. The energy consumption of the EVs is proportional to their cargo load which is an important constraint in real-world logistics applications. A new set of benchmark instances is proposed for the E-CVRP. As solution methods to these new benchmarks, we apply the ant colony optimization metaheuristic method and an exact method. Experimental results on the ECVRP demonstrate the high complexity of the problem and the efficiency of the applied metaheuristic solution method.

This paper proposes a general algorithm framework for solving dynamic sequence optimization problems (DSOPs). The framework adapts a novel genetic learning (GL) algorithm to dynamic environments via a clustering-based multi-population strategy with a memory scheme, namely, multi-population GL (MPGL). The framework is instantiated for a 3D dynamic shortest path problem, which is developed in this paper. Experimental comparison studies show that MPGL is able to quickly adapt to new environments and it outperforms several ant colony optimization variants.

Ant colony optimization (ACO) algorithms have proved to be suitable for solving dynamic optimization problems (DOPs). The integration of local search operators with ACO has also proved to significantly improve the output of ACO algorithms. However, almost all previous works of ACO in DOPs do not utilize local search operators. In this work, the MAX-MIN Ant System (MMAS), one of the best ACO variations, is integrated with advanced and effective local search operators, i.e., the Lin-Kernighan and the Unstringing and Stringing heuristics, resulting in powerful memetic algorithms. The best solution constructed by ACO is passed to the operator for local search improvements. The proposed memetic algorithms aim to combine the adaptation capabilities of ACO for DOPs and the superior performance of the local search operators. The travelling salesperson problem is used as the base problem to generate both symmetric and asymmetric dynamic test cases. Experimental results show that the MMAS is able to provide good initial solutions to the local search operators especially in the asymmetric dynamic test cases.

In this work we consider the scheduling problem for charging a fleet of electric vehicles (EVs) within a station such that the total tardiness of the problem is minimized. The generation of a feasible and efficient schedule is a difficult task due to the physical and power constraints of the charging station, i.e., the maximum contracted power and the maximum power imbalance between the lines of the electric feeder. The ant colony optimization (ACO) metaheuristic is applied to coordinate the charging process of the EVs within the charging station by generating efficient schedules. The behaviour and performance of ACO is analyzed and compared against state-of-the-art approaches on a benchmark set inspired by real-world scenarios. The experimental results show that the application of ACO is highly effective and outperforms other approaches.

This paper develops a novel control methodology for tracking control of robot manipulators based on a novel adaptive backstepping nonsingular fast terminal sliding mode control (ABNFTSMC). In this approach, a novel backstepping nonsingular fast terminal sliding mode controller (BNFTSMC) is developed based on an integration of integral nonsingular fast terminal sliding mode surface and a backstepping control strategy. The benefits of this approach are that the proposed controller can preserve the merits of the integral nonsingular fast terminal sliding mode control (NFTSMC) in terms of high robustness, fast transient response, and finite-time convergence, as well as backstepping control strategy in terms of globally asymptotic stability based on Lyapunov criterion. However, the major limitation of the proposed BNFTSMC is that its design procedure is dependent on the prior knowledge of the bound value of the disturbance and uncertainties. In order to overcome this limitation, an adaptive technique is employed to approximate the upper bound value; yielding an ABNFTSMC is recommended. The proposed controller is then applied for tracking control of a PUMA560 robot and compared with other state-of-the-art controllers, such as computed torque controller, PID controller, conventional PID-based sliding mode controller, and NFTSMC. The comparison results demonstrate the superior performance of the proposed approach.

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.

In this paper, the effect of the population size on the performance of the MAX-MIN ant system for dynamic optimization problems (DOPs) is investigated. DOPs are generated with the dynamic benchmark generator for permutation-encoded problems. In particular, the empirical study investigates: a) possible dependencies of the population size parameter with the dynamic properties of DOPs; b) the effect of the population size with the problem size of the DOP; and c) whether a larger population size with less algorithmic iterations performs better than a smaller population size with more algorithmic iterations given the same computational budget for each environmental change. Our study shows that the population size is sensitive to the magnitude of change of the DOP and less sensitive to the frequency of change and the problem size. It also shows that a longer duration in terms of algorithmic iterations results in a better performance.

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance.

Multipopulation methods are effective in solving dynamic optimization problems. However, to efficiently track multiple optima, algorithm designers need to address a key issue: how to adapt the number of populations. In this paper, an adaptive multipopulation framework is proposed to address this issue. A database is designed to collect heuristic information of algorithm behavior changes. The number of populations is adjusted according to statistical information related to the current evolving status in the database and a heuristic value. Several other techniques are also introduced, including a heuristic clustering method, a population exclusion scheme, a population hibernation scheme, two movement schemes, and a peak hiding method. The particle swarm optimization and differential evolution algorithms are implemented into the framework, respectively. A set of multipopulation-based algorithms are chosen to compare with the proposed algorithms on the moving peaks benchmark using four different performance measures. The effect of the components of the framework is also investigated based on a set of multimodal problems in static environments. Experimental results show that the proposed algorithms outperform the other algorithms in most scenarios.

This paper proposes a novel adaptive local search algorithm for tackling real-valued (or continuous) dynamic optimization problems. The proposed algorithm is a simple single-solution based metaheuristic that perturbs the variables separately to select the search direction for the following step and adapts its step size to the gradient. The search directions that appear to be the most promising are rewarded by a step size increase while the unsuccessful moves attempt to reverse the search direction with a reduced step size. When the environment is subject to changes, a new solution is sampled and crosses over the best solution in the previous environment. Furthermore, the algorithm makes use of a small archive where the best solutions are saved. Experimental results show that the proposed algorithm, despite its simplicity, is competitive with complex population-based algorithms for tested dynamic optimization problems.

Ant colony optimization (ACO) algorithms have proved to be able to adapt for solving dynamic optimization problems (DOPs). The integration of local search algorithms has also proved to significantly improve the output of ACO algorithms. However, almost all previous works consider stationary environments. In this paper, the MAX -MIN Ant System, one of the best ACO variations, is integrated with the unstringing and stringing (US) local search operator for the dynamic travelling salesman problem (DTSP). The best solution constructed by ACO is passed to the US operator for local search improvements. The proposed memetic algorithm aims to combine the adaptation capabilities of ACO for DOPs and the superior performance of the US operator on the static travelling salesman problem in order to tackle the DTSP. The experiments show that the MAX -MIN Ant System is able to provide good initial solutions to US and the proposed algorithm outperforms other peer ACO-based memetic algorithms on different DTSPs.

Dynamic optimization problems (DOPs) have been a major challenge for ant colony optimization (ACO) algorithms. The integration of ACO algorithms with immigrants schemes showed promising results on different DOPs. Each type of immigrants scheme aims to address a DOP with specific characteristics. For example, random and elitism-based immigrants perform well on severely and slightly changing environments, respectively. In this paper, two hybrid immigrants, i.e., non-interactive and interactive, schemes are proposed to combine the merits of the aforementioned immigrants schemes. The experiments on a series of dynamic travelling salesman problems showed that the hybridization of immigrants further improves the performance of ACO algorithms.

The integration of immigrants schemes with ant colony optimization (ACO) algorithms showed promising results on different dynamic optimization problems (DOPs). The principle of integrating immigrants schemes within ACO is to introduce newly generated ants that will replace other ants in the current population. One of the most advanced immigrants schemes is the elitism-based immigrants scheme, where the best ant from the previous environment is used as the base to generate immigrants. So far, the replacement rate used for elitism-based immigrants in ACO remained fixed during the execution of the algorithm. In this paper the impact of the replacement rate on the performance of ACO algorithms with elitism-based immigrants is examined. In addition, an adaptive replacement rate is proposed and compared with fixed and optimized replacement rates based on a series of DOPs. The experiments show that the adaptive scheme provides an automatic way to set a good value, although not the optimal one, for the replacement rate within ACO with elitism-based immigrants for DOPs.

One approach integrated with genetic algorithms (GAs) to address dynamic optimization problems (DOPs) is to maintain diversity of the population via introducing immigrants. Many immigrants schemes have been proposed that differ on the way new individuals are generated, e.g., mutating the best individual of the previous environment to generate elitism-based immigrants. This paper examines the performance of elitism-based immigrants GA (EIGA) with different immigrant mutation probabilities and proposes an adaptive mechanism that tends to improve the performance in DOPs. Our experimental study shows that the proposed adaptive immigrants GA outperforms EIGA in almost all dynamic test cases and avoids the tedious work of fine-tuning the immigrant mutation probability parameter.

A recent integration showed that ant colony optimization (ACO) algorithms with immigrants schemes perform well on different variations of the dynamic travelling salesman problem. In this paper, we address ACO for the dynamic vehicle routing problem (DVRP) with traffic factor where the changes occur in a cyclic pattern. In other words, previous environments will re-appear in the future. Memory-based immigrants are used with ACO in order to collect the best solutions from the environments and use them to generate diversity and transfer knowledge when a dynamic change occurs. The results show that the proposed algorithm, with an appropriate size of memory and immigrant replacement rate, outperforms other peer ACO algorithms on different DVRP test cases.