MADEA: multi-objective amended differential evolution algorithm

The aim of the current work is to modify the amended differential evolution algorithm (ADEA) to solve multi-objective optimisation problems. The modified ADEA algorithm is named MADEA. The single objective ADEA algorithm is employed with an efficient non-dominated search (ENS) method for finding the non-dominated solutions, a crowding distance technique for comparing the non-dominated solutions, and an archive that stores the non-dominated solutions. The above-mentioned modifications in ADEA resulted in an algorithm capable of solving benchmark functions given in CEC 2009 with competitive results. The performance of the MADEA is measured using inverted generational distance (IGD) and hypervolume (HV). The outcomes of performance measures are compared against MWDEO, MOEA/D, MOPSO, SMPSO, NSGA-II, SHAMODEWO, MOEADSTM and NSGA-III. The results show that MADEA has outperformed 60% of the problems in the test suite in IGD values and the results were found to be significantly similar to 20% of the competition.