DE-PSO: A NEW HYBRID META-HEURISTIC FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

2011 ◽  
Vol 07 (03) ◽  
pp. 363-381 ◽  
Author(s):  
MILLIE PANT ◽  
RADHA THANGARAJ ◽  
AJITH ABRAHAM
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Test Functions ◽  
Global Optimization Algorithm ◽  
Two Phase ◽  
Swarm Optimization ◽  
Life Problems

This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of Differential Evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving the considered test functions as well as real life problems.

scholarly journals Enriched Particle Swarm Optimization Created By Varying Parameters for Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 8259-8265
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Slow Convergence ◽  
Inertia Weight ◽  
Life Problems ◽  
Fixed Constant

Particle swarm optimization (PSO) is one of the most capable algorithms that reside to the swarm intelligence (SI) systems. Recently, it becomes very popular and renowned because of the easy implementation in complex/real life optimization problems. However, PSO has some observable drawbacks such as diversity maintenance, pre convergence and/or slow convergence speed. The ultimate success of PSO depends on the velocity update of the particles. Velocity has a significant dependence on its multiplied coefficient like inertia weight and acceleration factors. To increase the ability of PSO, this paper introduced an enriched PSO (namely ePSO), to solve hard optimization problems more precisely, efficiently and reliably. In ePSO novel gradually decreased inertia weight (as an alternative of a fixed constant value) and new gradually decreased and/or increased acceleration factors (meant for cognitive and social modules) is introduced. Proposed ePSO is used to solve four well known typical unconstrained benchmark functions and four complex unconstrained real life problems. The overall observation shows that proposed new algorithm ePSO is fitter than the compared algorithms significantly and statistically. Moreover, the convergence accuracy and speed of ePSO are also improved effectively

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