An improved poor and rich optimization algorithm

• Published in: PloS one Vol. 18; no. 2; p. e0267633
• Main Authors: Wang, Yanjiao, Zhou, Shengnan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.02.2023; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Cloning; Computer and Information Sciences; Computer Simulation; Convergence; Design; Ecology and Environmental Sciences; Engineering; Evaluation; Evolution; Evolution & development; Evolutionary algorithms; Genes; Genetic algorithms; Heuristic; Heuristic methods; Heuristics; Jargon; Learning; Mathematical optimization; Neighborhoods; Normal distribution; Optimization; Optimization algorithms; Parameter estimation; Physical Sciences; Population studies; Problem Solving; Research and Analysis Methods; Social Sciences; Terminology; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0267633

The poor and rich optimization algorithm (PRO) is a new bio-inspired meta-heuristic algorithm based on the behavior of the poor and the rich. PRO suffers from low convergence speed and premature convergence, and easily traps in the local optimum, when solving very complex function optimization problems. To overcome these limitations, this study proposes an improved poor and rich optimization (IPRO) algorithm. First, to meet the requirements of convergence speed and swarm diversity requirements across different evolutionary stages of the algorithm, the population is dynamically divided into the poor and rich sub-population. Second, for the rich sub-population, this study designs a novel individual updating mechanism that learns from the evolution information of the global optimum individual and that of the poor sub-population simultaneously, to further accelerate convergence speed and minimize swarm diversity loss. Third, for the poor sub-population, this study designs a novel individual updating mechanism that improves some evolution information by learning alternately from the rich and Gauss distribution, gradually improves evolutionary genes, and maintains swarm diversity. The IPRO is then compared with four state-of-the-art swarm evolutionary algorithms with various characteristics on the CEC 2013 test suite. Experimental results demonstrate the competitive advantages of IPRO in convergence precision and speed when solving function optimization problems.