NSCSO: a novel multi-objective non-dominated sorting chicken swarm optimization algorithm.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Mathematics

Background:

• Multi-objective optimization problems (MOP) present significant challenges in finding efficient and satisfactory solutions.
• Existing algorithms often struggle with the complexity and scale of MOPs.

Purpose of the Study:

• To propose a novel algorithm, Non-Dominated Sorting Chicken Swarm Optimization (NSCSO), for effectively solving MOPs.
• To enhance the exploration and exploitation capabilities in MOP algorithms.

Main Methods:

• Developed NSCSO by integrating fast non-domination sorting and crowding distance strategy into the Chicken Swarm Optimization (CSO) algorithm.
• Incorporated an elite opposition-based learning strategy to guide rooster individuals towards optimal solution directions.
• Evaluated NSCSO against six other algorithms using 15 benchmark functions and six engineering design problems.

Main Results:

• NSCSO exhibited significantly better performance compared to existing algorithms in solving MOPs across benchmark functions.
• The algorithm achieved competitive and realistic solutions for complex multi-objective engineering design problems.
• Statistical analysis (Friedman test) confirmed the superior performance of NSCSO.

Conclusions:

• NSCSO is a highly effective and robust algorithm for addressing multi-objective optimization challenges.
• The proposed method offers a promising approach for both theoretical MOP research and practical engineering applications.