A Multi Objective Evolutionary Algorithm based on Parallel Coordinates
Multi Objective Evolutionary Algorithms (MOEAs) are powerful tools for solving a wide range of real world applications that involve the simultaneous optimization of several objective functions. However, their scalability to many objective problems remains as an important issue since, due to the large number of non dominated solutions, the search is guided solely by the diversity criterion. In this paper, we propose a novel MOEA that incorporates a density estimator based on a visualization technique called Parallel Coordinates. Using this approach, a graph is represented by a digital image, where a pixel identifies the level of overlapping line segments and those individuals covering a wide area of the image have a high probability of survival. Experimental results indicate that our proposed approach, called Multi objective Optimizer based on Value Path (MOVAP) outperforms existing algorithms based on clustering (SPEA2) crowding distance (NSGA II) reference points (NSGA III) and the hypervolume indicator (HypE) on most of the problems of the WFG test suite for five and seven objectives, while its performance in low dimensionality remains competitive.
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