A Non-Revisiting Equilibrium Optimizer Algorithm

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that inspired by estimating dynamics and states in controlled volume mass balance models. As stochastic algorithm, EO inevitably produces duplicated solutions, which wasteful of valuable evaluation opportunities. In addition, an excessive number solutions can increase the risk getting trapped local optima. this paper, improved with bis-population-based non-revisiting (BNR) mechanism proposed, namely BEO. It aims to eliminate duplicate generated population during iterations, thus avoiding wasted Furthermore, when revisited solution detected, BNR activates its unique archive learning assist generating high-quality using excellent genes historical information, not only improves algorithm's diversity but also helps get out optimum dilemma. Experimental findings IEEE CEC2017 benchmark demonstrate proposed BEO outperforms other seven representative techniques, including original algorithm.