Pseudo-collision in swarm optimization algorithm and solution：rain forest algorithm

Abstract

Pseudo-collision (Pc) as a common but neglected phenomenon in swarm optimization algorithm is revealed in this paper. Mechanism analysis on the inevitability of Pc indicates that both the lack of relation among samples and the unconstrained behavior of sampling are the inherent character of agent operation causing Pc in state-of-the-art swarm algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO). Based on the result of mechanism analysis, a novel partition management and classification sampling strategy is proposed to reduce Pc. In addition, both uniform and non-uniform principles are designed to facilitate the trade-off between exploration and exploitation during optimization. Rain forest algorithm (RFA), of which the evolution mechanism is identical with the above strategy and the principles, is proposed in this paper. By examining the rapidity, accuraty, and generalization capability across six benchmark nonconvex functions, RFA is found to be competitive with or even superior to GA and PSO in dealing with complex multi-peak optimization.

Research status analysis of test reliability algorithm of three-phase asynchronous motor

“…The two kinds of filters have advantages and disadvantages, but the adaptive filter has more restrictions than the classical filter. In literature [31][32][33][34][35][36][37][38] , solutions to optimization problems are discussed, including traditional numerical analysis algorithm and intelligent bionic algorithm.…”

“…It studies the threshold control strategy of multi-level modular high-capacity high-pressure converter based on Tabu search optimization algorithm. In literature 35 , rainforest optimization algorithm was used to solve the problem that heuristic optimization algorithm fell into local optimal due to premature convergence.…”

Nuclear Reaction Optimization: A Novel and Powerful Physics-Based Algorithm for Global Optimization

“…Besides, various additional EAs have been proposed, such as genetic programming (GP) [13], evolutionary programming (EP) [14], evolution strategy (ES) [15] and the bird mating optimizer (BMO) [16]. Recently, more state-of-the-art EAs emerged, such as backtracking search optimization algorithm (BSA) [17], forest optimization algorithm (FOA) [18], interior search algorithm (ISA) [19], Jaya algorithm [20], rain forest algorithm (RFA) [21] and competitive optimization algorithm (COOA) [22].…”