PECSO: An Improved Chicken Swarm Optimization Algorithm with Performance-Enhanced Strategy and Its Application

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The standard Chicken Swarm Optimization Algorithm (CSO) suffers from low convergence accuracy, slow speed, and a tendency to fall into local optima.
• Addressing these limitations is crucial for improving the applicability of CSO in complex optimization tasks.

Purpose of the Study:

• To propose a performance enhancement strategy for the CSO algorithm (PECSO) to overcome its inherent deficiencies.
• To improve the diversity, exploration range, and balance between exploration and exploitation in the CSO algorithm.

Main Methods:

• Implemented a free grouping mechanism to establish a hierarchy, enhancing individual diversity and search space exploration.
• Introduced niche division with hens as centers, employing synchronous updating and spiral learning for better exploration-exploitation balance.
• Validated PECSO using the CEC2017 benchmark function and applied it to engineering optimization cases and robot inverse kinematics.

Main Results:

• PECSO demonstrated faster convergence, higher precision, and stronger stability compared to other algorithms on benchmark functions.
• The algorithm successfully obtained good solutions for three engineering optimization problems.
• PECSO showed a competitive effect in solving the inverse kinematics of robots.

Conclusions:

• The proposed PECSO algorithm effectively enhances CSO performance, offering significant improvements in convergence speed, accuracy, and stability.
• PECSO shows strong potential for practical applications, including engineering optimization and robot kinematics.