Experience Exchange Strategy: An evolutionary strategy for meta-heuristic optimization algorithms

Abstract

Meta-heuristic optimization algorithms typically change individual positions based on iterations, causing the population to switch search regions. This may result in the original search area not being explored in depth, thereby reducing the optimization performance of the algorithm. To deepen the connection between populations and individuals, this article proposes an evolutionary strategy called Experience Exchange Strategy (EES). EES considers the relationship between individuals and populations, deepening the connection between individuals and populations. EES has structured into three distinct stages: the experience scarcity stage (ESC), the experience crossover stage (ECR), the experience sharing stage (ESH). In the ESC, due to many areas not being searched, the population lacks search experience and mainly relies on primitive algorithms to find positions. This can preserve the optimization effect of the original algorithm and explore more positions. In the ECR, due to the accumulation of more experience in the population, individuals will update their positions based on more reference population experience. This can improve the accuracy of the search range and conduct more detailed searches. In the ESH, the population accumulates a large amount of experience, and individuals conduct more detailed searches based on the population’s experience. Through ESH, the population can search intensively to find a better position more finely. To verify the performance of EES, this article conducted optimization tests using IEEE CEC2014 and IEEE CEC2020 functions. And 15 algorithms were selected for improvement and compared with the original algorithm. Then, 57 single objective constrained engineering problems were used for testing experiments. The experimental results demonstrate that EES significantly improves the performance of meta-heuristic optimization algorithms.