Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons

Abstract

Abstract-The rapid advances of evolutionary methods for multi-objective (MO) optimization poses the difficulty of keeping track of the developments in this field as well as selecting an appropriate evolutionary approach that best suits the problem in-hand. This paper aims to analyze the strength and weakness of different evolutionary methods proposed in literatures. For this purpose, ten existing well-known evolutionary MO approaches have been experimented and compared exte nsively on two benchmark problems with different MO optimization difficulties and characteristics. Besides considering the usual two important aspects of MO performance, i.e., the spread across the Pareto-optimal front as well as the ability to attain the global optimum or final trade -offs, this paper also proposes a few useful performance measures for better and comprehensive examination of each approach both quantitatively and qualitatively. Simulation results for the comparisons are commented and summarized.

A multi-objective hyper-heuristic based on choice function

“…The idea behind the dominance concept is to generate a preference between MOP solutions since there is no information regarding the objective preference provided by the decision maker. Tan et al (2002) and Coello et al (2007) present a more formal definition of Pareto dominance.…”

“…As mentioned earlier, the measurement of the quality of the solution for multi-objective problems requires us to assess different aspects of the non-dominated set in the objective space. According to Tan et al (2002), no single MOEA excels in all performance measures. We employ a learning mechanism based on different measures using a ranking scheme to provide a feedback about the quality of the solutions.…”

“…However, the framework enables us to maintain relevant information on how each low level meta-heuristic performs. In order to provide some information as a feedback, regardless of the multi-objective problem, four performance metrics are maintained as shown in (Tan et al, 2002). The high level strategy selects one low level heuristic at each decision point according to the information obtained from the feedback mechanism.…”

“…• Algorithm effort (AE) (Tan et al, 2002): measures the computational effort of an algorithm to obtain the Pareto optimal set. It ranges from [0,∞).…”

Genetic programming for production scheduling: a survey with a unified framework

“…One advantage of using GP for designing heuristics is that their search mechanisms are very flexible and many advanced EC techniques [27,61,133] have been developed to cope with multiple objectives.…”