Micro-Genetic algorithm with fuzzy selection of operators for multi-Objective optimization: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>μ</mml:mi></mml:math>FAME

Santiago, Alejandro; Dorronsoro, Bernabè; Fraire, Héctor; Ruiz, Patricia

doi:10.1016/j.swevo.2020.100818

Cited by 20 publications

(4 citation statements)

References 40 publications

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“…The proposed DRL model regards the decision vectors as states and the operators as actions; then, the fitness improvement of the solution brought by the operator is taken as the reward. Alejandro et al [28] suggested a fuzzy selection of operators that chooses the most appropriate operators during evolution to promote both diversity and convergence of solutions. Dong et al [18] devised a test-and-apply structure to adaptively select the operator for decomposition-based MOEAs.…”

Section: B Adaptive Operator Selection In Mopsmentioning

confidence: 99%

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Ming

Gong

Gao

2024

IEEE Trans. Evol. Computat.

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Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention. Various constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been developed with the use of different algorithmic strategies, evolutionary operators, and constraint-handling techniques. The performance of CMOEAs may be heavily dependent on the operators used, however, it is usually difficult to select suitable operators for the problem at hand. Hence, improving operator selection is promising and necessary for CMOEAs. This work proposes an online operator selection framework assisted by Deep Reinforcement Learning. The dynamics of the population, including convergence, diversity, and feasibility, are regarded as the state; the candidate operators are considered as actions; and the improvement of the population state is treated as the reward. By using a Q-Network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance. The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems. The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

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Section: B Adaptive Operator Selection In Mopsmentioning

confidence: 99%

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Ming

Gong

Gao

2024

IEEE Trans. Evol. Computat.

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“…The quality of the non-dominated Pareto solutions obtained by the proposed algorithm is measured and compared with those of the MOGWO, MOSMA, NSGA-II, MOEA-D and MOPSO algorithms by using several performance metrics, namely the spacing metric (SM), the Cindex metric [48], Hypervolume (HV) [49][50][51] and the computational burden for the solutions. For a fair comparison, we set the parameters of MOPSO, MOEA-D, MOGWO, MOSMA, and NSGA-II to the values given in Table 7, where n in MOEA-D algorithm is the number of population.…”

Section: Comparison Of the Solution Algorithmsmentioning

confidence: 99%

A multi-objective framework for distributed energy resources planning and storage management

et al. 2022

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“…In [22] used the multi-objective strawberry algorithm and fuzzy algorithm to study the multi-objective system availability and cost optimization of the parallelseries system, a numerical case study involving 10 subsystems highlights the applicability of the proposed approach. In [23] and [24] used fuzzy systems to overcome the diversity loss caused by elitism and selected appropriate operators in the evolution process to increase the diversity and convergence of the solution. Liu et al [25] proposed a fuzzy decomposition-based MOEA, which uses a fuzzy system to estimate the shape of the population and extracts minimum similar solutions as weight vectors to obtain constrained fuzzy sub-problems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive NSGA-III for Large-Scale Optimization Problems

Zhang

Xie

Wang

2022

Int. J. Fuzzy Syst.

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More and more multi-objective evolutionary algorithms are proposed and used to solve many-objective optimization problems and large-scale optimization problems. However, most of the existing algorithms use fixed crossover probability (pc) and mutation probability (pm) in the generation process of offspring, which makes the performance of the algorithm poor when dealing with complex problems. In this paper, by analyzing the complex nonlinear relationship between performance metrics and pc and pm in the search process of many-objective evolutionary algorithms. A fuzzy inference system is constructed to dynamically update the pc and pm in the iterative process. Therefore, a fuzzy adaptive NSGA-III algorithm is proposed and used to solve large-scale optimization problems. For the construction of fuzzy systems, this paper takes the number of iterations, convergence metric, and diversity metric as inputs, and pc and pm as outputs. Four different fuzzy systems are obtained, and the best fuzzy system is selected through experiments. In order to further verify the effectiveness of the algorithm, the proposed algorithm and the existing literature are tested on LSMOP problems. The results show that the fuzzy system can well describe the complex non-linear relationship between the performance metrics and the pc and the pm in the search process of the many-objective evolutionary algorithm. It also effectively improves the performance of the algorithm when solving large-scale optimization problems, resulting in maintenance of the convergence and diversity of the population.

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Micro-Genetic algorithm with fuzzy selection of operators for multi-Objective optimization: μFAME

Cited by 20 publications

References 40 publications

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer

A multi-objective framework for distributed energy resources planning and storage management

Fuzzy Adaptive NSGA-III for Large-Scale Optimization Problems

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