Multi-Objective Differential Evolution Algorithm with a New Environmental Parameter based Mutation for Solving Optimization Problems

Simultaneous optimization of two or more objectives is an instance multi-objective (MOO). However, for most the Multi-Objective Problems (MOPs), no single solution can optimize all objective functions simultaneously. Evolutionary algorithms are a type algorithm used constructing well-distributed optimal front quickly than much efficient approach. One commonly in this regard differential evolution (DE). To solve problem non-dominated sorting DE (MODE), approach proposed that reduces time complexity. recognized as one best methods solving MOPs. Several versions DEs have been This article proposes novel mutation technique, named Environmental Parameter-based Differential Evolution (EP-MODE) acquires additional environmental parameters to preserve diversity and accelerate convergence. The also complexity (MODE). mutation's performance has evaluated on DTLZ series ZDT benchmark test Pareto-optimal compared with existing (MODE MODE-RMO). comparatively verifies EP-MODE outperforms MODEs lower dimension higher functions. HIGHLIGHTS New parameters-based introduced problems (EP-mode) addition EP-mode found be appropriate maintaining while accelerating convergence rate bi-objective (DTLZ series) tri-objective (ZDT EP-MODE's efficiency MODE ranking-based operator GRAPHICAL ABSTRACT