An Effective Dynamical Multi-objective Evolutionary Algorithm for Solving Optimization Problems with High Dimensional Objective Space

Autor:	Minzhong Liu, Lishan Kang, Xiufen Zou
Rok vydání:	2007
Předmět:	Continuous optimization education.field_of_study Mathematical optimization Vector optimization Meta-optimization Population Evolutionary algorithm Imperialist competitive algorithm Interactive evolutionary computation education Multi-objective optimization Mathematics
Zdroj:	Advances in Computation and Intelligence ISBN: 9783540745808 ISICA
DOI:	10.1007/978-3-540-74581-5_9
Popis:	An effective dynamical multi-objective evolutionary algorithm (DMOEA) based on the principle of the minimal free energy in thermodynamics was proposed in the paper. It provided a new fitness assignment strategy based on the principle of free energy minimization of thermodynamics for the convergence of solves, introduced a density-estimate technique for evaluating the crowding distance between individuals and a new criterion for selection of new individuals to maintain the diversity of the population. By using multi-crossover operator, it improved the search efficiency and the robustness. The test example results proves the validity of the algorithm in its rapidly convergence and maintaining diversity.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0904a7814cb669ad7354d8cc38d7f6e4 https://doi.org/10.1007/978-3-540-74581-5_9 Zobrazit plný text záznamu