Abstract
The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems.
Highlights
Constrained multi-objective optimization problems (CMOPs) are commonly found in the field of engineering optimization, such as robot’s design optimization [1], compressedair station scheduling problem [2] and scheduling optimization of microgrid [3]
There is no significant difference between ACMODE and ACHT-CMODE on CF4
An adaptive constrained multi-objective differential evolution algorithm based on state–action–reward–state–action approach (ACMODE) is introduced to implement adaptation of constraint handling techniques (CHTs) and generation strategies, which can be automatically selected via a SARSA method
Summary
Constrained multi-objective optimization problems (CMOPs) are commonly found in the field of engineering optimization, such as robot’s design optimization [1], compressedair station scheduling problem [2] and scheduling optimization of microgrid [3]. To effectively solve CMOPs, various improved CMOEAs have been proposed. Wang et al [4] proposed a cooperative multi-objective evolutionary algorithm with a propulsive population (CMOEA-PP) to achieve a tradeoff among the diversity, the convergence, and the feasibility in different evolutionary stages. Datta et al [5] combined the evolutionary multi-objective optimization method with the penalty function method, and proposed a bi-objective hybrid constrained optimization algorithm (HyCon) to deal with CMOPs. Yuan et al [6] proposed an indicator-based evolutionary algorithm to prevent the population from falling into local areas. Cui et al [7] proposed an adaptive constraint handling technique (CHT), which can adaptively select suitable CHT from three state-of-the-art CHTs via the Q-learning method
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