Cooperative particle swarm optimizer with depth first search strategy for global optimization of multimodal functions

2022 ◽  
Author(s):  
Jie Wang ◽  
Yongfang Xie ◽  
Shiwen Xie ◽  
Xiaofang Chen
Keyword(s):  
Global Optimization ◽  
Search Strategy ◽  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Depth First Search ◽  
Multimodal Functions

Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

2015 ◽  
Vol 24 (05) ◽  
pp. 1550017 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Akugbe Martins Arasomwan
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Search Range ◽  
Original Algorithm ◽  
Inertia Weight ◽  
And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

An enhanced class topper algorithm based on particle swarm optimizer for global optimization

2020 ◽  
Author(s):  
Alfred Adutwum Amponsah ◽  
Fei Han ◽  
Qing-Hua Ling ◽  
Patrick Kwaku Kudjo
Keyword(s):  
Global Optimization ◽  
Particle Swarm ◽  
Particle Swarm Optimizer
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