Modified bare bones particle swarm optimization with differential evolution for large scale problem

2016 ◽  
Author(s):  
Shiqin Yang ◽  
Yuji Sato
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Large Scale ◽  
Particle Swarm ◽  
Scale Problem ◽  
Swarm Optimization ◽  
Large Scale Problem ◽  
Bare Bones

Evaluation of integrated differential evolution and unified bare-bones particle swarm optimization for phase equilibrium and stability problems

2011 ◽  
Vol 310 (1-2) ◽  
pp. 129-141 ◽  
Author(s):  
Haibo Zhang ◽  
Jorge Adán Fernández-Vargas ◽  
Gade Pandu Rangaiah ◽  
Adrián Bonilla-Petriciolet ◽  
Juan Gabriel Segovia-Hernández
Keyword(s):  
Particle Swarm Optimization ◽  
Phase Equilibrium ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Stability Problems ◽  
Bare Bones

scholarly journals Heterogeneous Cooperative Bare-Bones Particle Swarm Optimization with Jump for High-Dimensional Problems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1539
Author(s):  
Joonwoo Lee ◽  
Won Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Particle Swarm ◽  
Optimization Method ◽  
High Dimensional ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Optima ◽  
One Step ◽  
Bare Bones

This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.

scholarly journals Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

2021 ◽  
Vol 11 (6) ◽  
pp. 2703
Author(s):  
Warisa Wisittipanich ◽  
Khamphe Phoungthong ◽  
Chanin Srisuwannapa ◽  
Adirek Baisukhan ◽  
Nuttachat Wisittipanit
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms ◽  
Current Practices

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

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