Multi-task multi-objective optimization problems need to consider the algorithm's convergence and the population's diversity. The information transfer of decisionvariables with different characteristics may h...
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Multi-task multi-objective optimization problems need to consider the algorithm's convergence and the population's diversity. The information transfer of decisionvariables with different characteristics may harm the effect of knowledge reuse. This paper proposes a novel hybrid multi-objective multifactorial memetic algorithm to address this issue. The proposed variableclassification method will classify decisionvariables into convergence-related and diversity-related decisionvariables. Only the same type of decisionvariables in the source and target tasks can transfer information to avoid negative transfer. Different evolutionary operators are adopted according to the characteristics of decisionvariables during individual recombination. In addition, the proposed algorithm hybridizes the immune algorithm as the global evolutionary operator and the evolutionary gradient search algorithm as the local search operator into the multifactorial framework to enhance the searching ability. Finally, the proposed algorithm is compared with the state-of-the-art multi-objective evolutionary multitasking algorithms. The results of the experiments show that the proposed algorithm can achieve promising performance on the classical and complex multi-task multi-objective benchmark test suites.
作者：
Sun, HaoWang, CongLi, XiaxiaHu, ZiyuYanshan Univ
Minist Educ Intelligent Control Syst & Intelligent Equipment Engn Res Ctr Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ
Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China
Dynamic multiobjective optimization problems (DMOPs) are constantly changing over time, which re-quires algorithms to keep track of the location of the Pareto optimal front (POF) at different moments in time. In this ...
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Dynamic multiobjective optimization problems (DMOPs) are constantly changing over time, which re-quires algorithms to keep track of the location of the Pareto optimal front (POF) at different moments in time. In this work, a decision variable classification strategy based on the degree of environmental change (DVCEC) is proposed. To accurately capture the occurrence of environmental changes, DVCEC designs an adaptive change detection method based on multiple regions. Since environmental changes affect each decisionvariable to different degrees, DVCEC classifies decisionvariables into several types and applies an appropriate prediction method to each type. In addition, an adjustment strategy is developed to minimize the impact of inaccurate predictions. The proposed DVCEC is evaluated on 22 benchmark problems and compared with four algorithms. Statistical results show that DVCEC can quickly approach POF and uniformly distribute it in most problems.(c) 2023 Elsevier B.V. All rights reserved.
In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorpo...
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In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multiobjective evolutionary algorithms. Maintaining a good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a DMOEA based on decision variable classification (DMOEA-DVC) is proposed in this article. DMOEA-DVC divides the decisionvariables into two and three different groups in static optimization and changes response stages, respectively. In static optimization, two different crossover operators are used for the two decisionvariable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decisionvariable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. The experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms.
This paper proposes a new decision variable classification-based cooperative coevolutionary algorithm, which uses the information of decision variable classification to guide the search process, for handling dynamic m...
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This paper proposes a new decision variable classification-based cooperative coevolutionary algorithm, which uses the information of decision variable classification to guide the search process, for handling dynamic multiobjective problems. In particular, the decisionvariables are divided into two groups: convergence variables (CS) and diversity variables (DS), and different strategies are introduced to optimize these groups. Two kinds of subpopulations are used in the proposed algorithm, i.e., the subpopulations that represent DS and the subpopulations that represent CS. In the evolution process, the coevolution of DS and CS is carried out through genetic operators, and subpopulations of CS are gradually merged into DS, which is optimized in the global search space, based on an indicator to avoid becoming trapped in local optimum. Once a change is detected, a prediction method and a diversity introduction approach are adopted for these two kinds of variables to get a promising population with good diversity and convergence in the new environment. The proposed algorithm is tested on 16 benchmark dynamic multiobjective optimization problems, in comparison with state-of-the-art algorithms. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization. (C) 2021 Elsevier Inc. All rights reserved.
In dynamic constrained multiobjective optimization problems (DCMOPs), dynamics may arise from time-varying objective functions or/and constraints. To solve these problems, maintaining a good balance among feasibility,...
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In dynamic constrained multiobjective optimization problems (DCMOPs), dynamics may arise from time-varying objective functions or/and constraints. To solve these problems, maintaining a good balance among feasibility, convergence and diversity of a population under dynamic environments is a critical challenge. Although appropriately utilizing the characteristic of decisionvariables can promote algorithms to better track the Pareto optima under dynamic environments, their sensitivity to constraints is neglected. Therefore, a dynamic constrained multiobjective evolutionary algorithm based on decision variable classification (DC-MOEA-DVC) is proposed. Under each environment, decisionvariables are classified into four types in terms of their influence on convergence, distribution, and constraint violation. Based on them, a new offspring generation method is developed, decisionvariables with different characteristics are rationally combined to generate offspring, with the purpose of accelerating the convergence of the population. Once an environmental change appears, a hybrid strategy consisting of four change response techniques is introduced for the corresponding types of decisionvariables, producing a new initial population. The experimental results show that DC-MOEA-DVC is superior to the other five state-of-the-art algorithms.
This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changin...
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This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a feedforward neural network (FNN) along with special points to generate an initial population. The FNN is trained with historical special points (knee point, boundary point, center point), and the current special points are generated by the FNN when an environmental change is detected. Then the decisionvariables are classified into convergence variables and diversity variables. The convergence variables of special points are locally searched to form a new population and the best individuals of this population are selected. Finally, a portion of the initial population is generated by conducting a local search on the diversity variables of best individuals, while the remaining portion is produced using random strategies. SPNN-DMOA only needs to maintain non-dominated solutions in proximity to special points, which reduces the computational complexity in the dynamic evolution process. The proposed algorithm has been compared with other state-of-the-art algorithms on a series of benchmark problems, demonstrating its superior performance in optimizing DMOPs.
In large-scale multi-objective optimization problems (LSMOPs), multiple conflicting objectives and hundreds even thousands of decisionvariables are contained. Therefore, it is a great challenge to address LSMOPs due ...
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In large-scale multi-objective optimization problems (LSMOPs), multiple conflicting objectives and hundreds even thousands of decisionvariables are contained. Therefore, it is a great challenge to address LSMOPs due to the curse of dimensionality. To tackle LSMOPs, this paper proposes a resource allocation-based multi-objective optimization evolutionary algorithm. In the proposed algorithm, decisionvariables are firstly divided into convergence-related variables and diversity-related variables by the proposed layer thickness-based variableclassification (LTVC) method. Then, a resource allocation-based convergence optimization strategy is introduced for the convergence-related variables, which can allocate more computational resource to the sub-component with the best contribution. For the diversity-related variables, diversity optimization technique is adopted. Finally, the experimental results verify that the proposed algorithm has a competitive performance compared with some state-of-the-art algorithms.
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