Abstract
This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.
Highlights
The progress of optimizing multiple mutually conflicting objectives simultaneously and obtaining a set of tradeoff solutions is regarded as Multi-objective optimization problems (MOPs) [1], which involves different fields, including controller design [2], weapon selection [3] and machine learning [4]
This shows that the designed prediction strategies can generate good population tracking the true Pareto-optimal front (POF) closely in dynamic environments
This paper proposed a new dynamic multiobjective optimization algorithm, named fitting-based prediction (FBP), for dealing with multiobjective problems in changing environments
Summary
The progress of optimizing multiple mutually conflicting objectives simultaneously and obtaining a set of tradeoff solutions is regarded as Multi-objective optimization problems (MOPs) [1], which involves different fields, including controller design [2], weapon selection [3] and machine learning [4]. Various multiobjective optimization algorithms have been proposed for solving MOPs successfully. Considering a minimization multiobjective optimization problem as follows, min x2O FðxÞ 1⁄4 ðf1ðxÞ; f2ðxÞ;. ; fmðxÞÞT ð1Þ where O QD i1⁄41 1⁄2Li; Ui. RD is the feasible area of the decision space, and. F consists of m time-varying objective functions. . ., xD) defines the decision vector involving D variables, Li and Ui represent the lower and upper bounds of the ith variable xi, respectively.
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