An improved cockroach swarm optimization.

Area of Science:

• Computational Intelligence
• Swarm Intelligence Algorithms
• Optimization Techniques

Background:

• The Cockroach Swarm Optimization (CSO) algorithm, a bio-inspired metaheuristic, is effective but can be improved.
• Existing CSO variants like MCSO, RIO, and HRIO have addressed some limitations.
• Enhancing population diversity and search capabilities remains a key challenge in swarm optimization.

Purpose of the Study:

• To introduce a novel hunger component into the CSO algorithm.
• To develop an Improved Cockroach Swarm Optimization (ICSO) algorithm.
• To evaluate the performance of ICSO against existing CSO variants on benchmark problems.

Main Methods:

• The original CSO model components (chase-swarming, dispersion, ruthless) were retained.
• A new hunger component was mathematically modeled using partial differential equations (PDE).
• The proposed ICSO algorithm was tested on standard benchmark functions and compared with CSO, MCSO, RIO, and HRIO.

Main Results:

• The ICSO algorithm consistently outperformed existing CSO, MCSO, RIO, and HRIO algorithms.
• The addition of the hunger component significantly improved the search ability and population diversity of the CSO algorithm.
• Benchmark test results validated the superior performance of the proposed ICSO.

Conclusions:

• The novel hunger component effectively enhances the cockroach swarm optimization algorithm.
• The Improved Cockroach Swarm Optimization (ICSO) algorithm offers superior performance and diversity.
• ICSO represents a significant advancement in swarm intelligence for optimization problems.