A Benchmark Test Suite for Dynamic Evolutionary Multiobjective Optimization

Growing trend of the dynamic multiobjective optimization research in the evolutionary computation community has increased the need for challenging and conceptually simple benchmark test suite to assess the optimization performance of an algorithm. This paper proposes a new dynamic multiobjective benchmark test suite which contains a number of component functions with clearly defined properties to assess the diversity maintenance and tracking ability of a dynamic multiobjective evolutionary algorithm (MOEA). Time-varying fitness landscape modality, tradeoff connectedness, and tradeoff degeneracy are considered as these properties rarely exist in the current benchmark test instances. Cross-problem comparative study is presented to analyze the sensitivity of a given algorithm to certain fitness landscape properties. To demonstrate the use of the proposed benchmark test suite, three evolutionary multiobjective algorithms, namely nondominated sorting genetic algorithm, decomposition-based MOEA, and recently proposed Kalman-filter-based prediction approach, are analyzed and compared. Besides, two problem-specific performance metrics are designed to assess the convergence and diversity performances, respectively. By applying the proposed test suite and performance metrics, microscopic performance details of these algorithms are uncovered to provide insightful guidance to the algorithm designer.

I. Introduction

Dynamic multiobjective optimization has recently received increasing attentions in the evolutionary computation research community [1]. One major reason for its rising popularity is probably because of its potential applications in various industries [2]–[5] and the recent advancement of static MOEA [6]–[11]. Existing approaches to solve the dynamic multiobjective optimization problem (DMOP) include random reinitialization method [12], memory-based method [13], prediction-based method [14]–[16], coevolutionary algorithm [17], multistrategy approach [18], and others [19]–[22].