A comparison on the search of particle swarm optimization and differential evolution on multi-objective optimization

2011 ◽  
Author(s):  
Jorge S. Hernandez Dominguez ◽  
Gregorio Toscano Pulido
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective

scholarly journals Multi-Objective Optimization of Planetary Gearbox with Adaptive Hybrid Particle Swarm Differential Evolution Algorithm

2021 ◽  
Vol 11 (3) ◽  
pp. 1107
Author(s):  
Miloš Sedak ◽  
Božidar Rosić
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Planetary Gearbox ◽  
Multi Objective ◽  
Mutation Operators ◽  
Evolution Algorithm

This paper considers the problem of constrained multi-objective non-linear optimization of planetary gearbox based on hybrid metaheuristic algorithm. Optimal design of planetary gear trains requires simultaneous minimization of multiple conflicting objectives, such as gearbox volume, center distance, contact ratio, power loss, etc. In this regard, the theoretical formulation and numerical procedure for the calculation of the planetary gearbox power efficiency has been developed. To successfully solve the stated constrained multi-objective optimization problem, in this paper a hybrid algorithm between particle swarm optimization and differential evolution algorithms has been proposed and applied to considered problem. Here, the mutation operators from the differential evolution algorithm have been incorporated into the velocity update equation of the particle swarm optimization algorithm, with the adaptive population spacing parameter employed to select the appropriate mutation operator for the current optimization condition. It has been shown that the proposed algorithm successfully obtains the solutions of the non-convex Pareto set, and reveals key insights in reducing the weight, improving efficiency and preventing premature failure of gears. Compared to other well-known algorithms, the numerical simulation results indicate that the proposed algorithm shows improved optimization performance in terms of the quality of the obtained Pareto solutions.

scholarly journals A Distributed Multifactorial Particle Swarm Optimization Approach

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Beta Function ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New System

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>

Multi-objective optimization of water distribution networks using particle swarm optimization

2021 ◽  
Vol 218 ◽  
pp. 18-31
Author(s):  
Douglas F. Surco ◽  
Diogo H. Macowski ◽  
Flávia A.R. Cardoso ◽  
Thelma P.B. Vecchi ◽  
Mauro A.S.S. Ravagnani
Keyword(s):  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective
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