A Comparison Test Performance for The Enhanced Hyper-Angle Exploitative Searching Algorithm

Multi-Objective Evolutionary Algorithms (MOEAs) maximize multiple objective functions using heuristic random searching to identify a collection of non-dominated solutions. In particular, multi-objective searching ranks solutions based on a subset of non-dominated solutions. The state-of-the-art, which is one of the evolutionary algorithms, is the second edition of the classical Fast Non-dominated Sorting Genetic Algorithm (NSGAII). However, the selection operator was enhanced and developed for optimal performance. This article shows the performance of the enhanced NSGA-II from the Pareto front and the number of non-dominated solutions on the basis of the Fonseca-Fleming problem (FON). The proposed enhancement was showing 100% performance in the comparison of the founded solutions with the benchmarks.