Abstract
ABSTRACTMetaheuristics have recently been commonly used to solve complex problems in real applications. Some scholars used multiple populations in metaheuristic approaches to shorten the execution time in finding nearly optimal solutions. In this paper, we revisit the properties of sub-population execution for genetic algorithm (GA) and design a level-wise hierarchical sub-population architecture. We also make experiments for comparing the performance of the architecture for unimodal and multimodal functions. The experimental results show that using the hierarchical GA architecture produces a more significant improvement on multimodal functions than on unimodal ones.
Highlights
In the past decades, many metaheuristics were generated from very simple concepts of nature
We first propose an algorithm called hierarchical multi-population genetic algorithm (HMGA), in which, the execution process is represented as a tree structure and conducted in a level-wise manner
When the termination criterion is met, the sub-populations that belong to the same parent node are merged and conceptually placed in the parent node
Summary
Many metaheuristics were generated from very simple concepts of nature. The second type of metaheuristics is based on animals’ behaviours These algorithms mainly mimic the social behaviour of swarms of creatures in nature. The third type of metaheuristics is from the observation of physical phenomena This kind of algorithms randomly generates a set of agents to search and move throughout. We first propose an algorithm called hierarchical multi-population genetic algorithm (HMGA), in which, the execution process is represented as a tree structure and conducted in a level-wise manner. Holland firstly introduced the GAs in 1975 (Holland, 1975) It is categorized as a global search metaheuristic that mimics the process of natural selection. GAs is a particular class of evolutionary algorithms, which generate solutions using the techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover ( called recombination). A GA executes the required crossover and mutation operators to make the population evolve according to the specified evaluation function
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