An improved multi-objective particle swarm optimization

Abstract

In this paper, a multi-objective particle swarm optimization based on extremal optimization with hybrid mutation and time-varied inertia (HM-TVWF-MOEPSO) method has been proposed in order to solve some of problems in the multi-objective particle swarm optimization and improve the performance of the algorithm.

Multi-objective construction machinery group configuration optimization based on improved particle swarm optimization algorithm

“…𝐶 = (𝑛 𝑤 𝐶 𝑤 + 𝑛 𝑡 𝐶 𝑡 + 𝑛 𝑟 𝐶 𝑟 + 𝑛 𝑏 𝐶 𝑏 )𝐷 (10) Where C is the project construction cost; Nw is the number of excavators; Nt is the number of tippers; Nr is the number of rollers; Nb is the number of bulldozers; Cw is the machine shift fee for excavators; Ct is the unit shift fee for dump trucks; Cr is the machine shift fee for roller; Cb is unit shift fee for bulldozers.…”

New hybrid approach HNSGA-III&SPEA/R: Application to optimization of powertrain mount system stiffness parameters

“…Therefore, new hybrid algorithms need to be proposed to be able to solve new problems that have not been resolved before and/or to have better accuracy than existing techniques. Some methods of hybridization of optimal algorithms have been developed recently: Jeong et al 26 proposed the development and investigation of Efficient GA/PSO-hybrid algorithm applicable to real-world design optimization; Premalatha et al 20 proposed Hybrid PSO and GA for global maximization; Bai 27 proposed a way to optimize multi-target particles based on extreme optimization with variable and inertial inertia mutations to improve the performance of the algorithm while solving some problems in multi-target particle optimization. A new hybrid heuristic optimization method is published in the current work for multi-purpose issues.…”

“…26 proposed the development and investigation of Efficient GA / PSO-hybrid algorithm applicable to real-world design optimization; Premalatha et al. 20 proposed Hybrid PSO and GA for global maximization; Bai 27 proposed a way to optimize multi-target particles based on extreme optimization with variable and inertial inertia mutations to improve the performance of the algorithm while solving some problems in multi-target particle optimization. A new hybrid heuristic optimization method is published in the current work for multi-purpose issues.…”

New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles

“…A multi-objective particle optimization method based on extreme optimization with variable and inertial inertia mutations (HM-TVWF-MOEPSO) has been proposed to solve some of the problems in optimization, multi-purpose particle chemistry, and improved algorithm performance. 30 A new hybrid heuristic algorithm is published in the current work for multi-objective optimization issues. The hybrid algorithm has proposed a method to combine the simple algorithm Nelder-Mead with the non-dominant genetic algorithm II (NSGA II) to find the best global point.…”