Solving dynamic multi-objective problems with a new prediction-based optimization algorithm

Funding Information: This work is supported by the National Natural Science Foundation of China under Grants 62006103 and 61872168, in part by the Jiangsu national science research of high education under Grand 20KJB110021. The authors express sincerely appreciation to the anonymous reviewers for their helpful opinions.Peer reviewedPublisher PD

A novel evolutionary algorithm for dynamic constrained multiobjective optimization problems

The file attached to this record is the author's final peer reviewed version.To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity–concavity and connectedness–disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. Experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms

Genetic algorithms with adaptive immigrants for dynamic environments

One approach integrated with genetic algorithms (GAs) to address dynamic optimization problems (DOPs) is to maintain diversity of the population via introducing immigrants. Many immigrants schemes have been proposed that differ on the way new individuals are generated, e.g., mutating the best individual of the previous environment to generate elitism-based immigrants. This paper examines the performance of elitism-based immigrants GA (EIGA) with different immigrant mutation probabilities and proposes an adaptive mechanism that tends to improve the performance in DOPs. Our experimental study shows that the proposed adaptive immigrants GA outperforms EIGA in almost all dynamic test cases and avoids the tedious work of fine-tuning the immigrant mutation probability parameter. © 2013 IEEE