An improved particle swarm optimization combined with double-chaos search

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• Standard Particle Swarm Optimization (PSO) faces challenges with population diversity and premature convergence due to reliance on global best particle.
• Local optima can trap standard PSO, limiting its effectiveness in complex optimization tasks.
• Chaos search mechanisms offer potential for global search and escaping local optima through chaotic dynamics.

Purpose of the Study:

• To propose an improved Particle Swarm Optimization (PSO) algorithm, termed Double-Chaos Search PSO (DCS-PSO).
• To address limitations of standard PSO, specifically premature convergence and lack of population diversity.
• To enhance optimization performance by integrating a double-chaos search mechanism.

Main Methods:

• Introduced a double-chaos search mechanism to narrow the search space in PSO.
• Integrated the logistic map for global search within the narrowed search space to enhance population diversity.
• Utilized the best solution from both logistic and population searches to guide convergence.

Main Results:

• DCS-PSO effectively narrows the search space, focusing the swarm on promising regions.
• The proposed method demonstrates improved convergence accuracy compared to standard PSO in most tested scenarios.
• Experimental results indicate enhanced convergence speed for DCS-PSO.

Conclusions:

• DCS-PSO overcomes the limitations of standard PSO by improving population diversity and avoiding local optima.
• The integration of double-chaos search significantly enhances the efficiency and effectiveness of Particle Swarm Optimization.
• DCS-PSO offers a promising approach for tackling complex optimization problems with better accuracy and speed.