dynamic constrained multiobjective optimization problems include irregular and discontinuous feasible regions, segmented true Pareto front, and dynamic environments. To address these problems, we design a dynamic cons...
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dynamic constrained multiobjective optimization problems include irregular and discontinuous feasible regions, segmented true Pareto front, and dynamic environments. To address these problems, we design a dynamic constrained multiobjective optimization algorithm based on dual-population evolution. This algorithm includes two populations, P-1 and P-2, based on the feasibility of solutions. It utilizes valuable information from infeasible solutions to drive the populations toward the feasible regions and the true Pareto front. At the same time, we propose a mating selection operator to facilitate information exchange between populations and generate promising offspring solutions. To respond to environmental changes, we design a strategy that combines new solutions obtained by the sampling-selection-resampling method and updated old ones, rapidly generating a promising population in a new environment. Additionally, we also design a test suit that can effectively present the discontinuous feasible regions and the irregular changes of true Pareto front in practical appcation problems. The results from experiments demonstrate the efficacy of the test suit, and the proposed algorithm exhibits competitiveness compared to other algorithms.
dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary computation community. To evaluate the capability of an algorithm i...
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dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary computation community. To evaluate the capability of an algorithm in solving dynamic constrained multiobjective optimization problems (DCMOPs), artificial test problems play a fundamental role. Nevertheless, some characteristics of real-world scenarios are not fully considered in the previous test suites, such as time-varying size, location, and shape of feasible regions, the controllable change severity, as well as small feasible regions. Therefore, we develop the generators of objective functions and constraints to facilitate the systematic design of DCMOPs, and then a novel test suite consisting of nine benchmarks, termed as DCP, is put forward. To solve these problems, a dynamicconstrainedmultiobjective evolutionary algorithm (DCMOEA) with a two-stage diversity compensation strategy (TDCEA) is proposed. Some initial individuals are randomly generated to replace historical ones in the first stage, improving the global diversity. In the second stage, the increment between center points of Pareto sets in the past two environments is calculated and employed to adaptively disturb solutions, forming an initial population with good diversity for the new environment. Intensive experiments show that the proposed test problems enable a good understanding of strengths and weaknesses of algorithms, and TDCEA outperforms other state-of-the-art comparative ones, achieving promising performance in tackling DCMOPs.
dynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directio...
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dynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directions and magnitudes of the optimal distance variables due to diverse changing environments. To solve these problems, we propose a multipopulation evolution-based dynamic constrained multiobjective optimization algorithm. In this algorithm, we design a tribe classification operator to divide the population into different tribes according to a feasibility check and the objective values, which is beneficial for driving the population toward the feasible region and Pareto-optimal fronts. Meanwhile, a population selection strategy is proposed to identify promising solutions from tribes and exploit them to update the population. The optimal values of the distance variables vary differently with dynamic environments, thus, we design a dynamic response strategy for solutions in different tribes that estimates their distances to approach the Pareto-optimal fronts and regenerates a promising population when detecting environmental changes. In addition, a scalable generator is designed to simulate diverse movement directions and magnitudes of the optimal distance variables in real-world problems under dynamic environments, obtaining a set of improved test problems. Experimental results show the effectiveness of test problems, and the proposed algorithm is impressively competitive with several chosen state-of-the-art competitors.
To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., con...
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To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity-concavity and connectedness-disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms.
dynamic constrained multiobjective optimization problems (DCMOPs) have gained increasing attention in the evolutionary computation field during the past years. Among the existing studies, it is a significant challenge...
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dynamic constrained multiobjective optimization problems (DCMOPs) have gained increasing attention in the evolutionary computation field during the past years. Among the existing studies, it is a significant challenge to rationally utilize historical knowledge to track the changing Pareto optima in DCMOPs. To address this issue, a subspace-knowledge transfer based dynamicconstrainedmultiobjective evolutionary algorithm is proposed in this article, termed SKTEA. Once a new environment appears, objective space is partitioned into a series of subspaces by a set of uniformly-distributed reference points. Following that, a subspace that has complete time series under certain number of historical environments is regarded as the feasible subspace by the subspace classification method. Otherwise, it is the infeasible one. Based on the classification results, a subspace-driven initialization strategy is designed. In each feasible subspace, Kalman filter is introduced to predict an individual in terms of historical solutions preserved in external storage. The predicted individuals of feasible neighbors are transferred into the infeasible subspace to generate the one, and then an initial population at the new time is formed by integrating predicted and transferred individuals. Intensive experiments on 10 test benchmarks verify that SKTEA outperforms several state-of-the-art DCMOEAs, achieving good performance in solving DCMOPs.
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