An Enhanced Differential Evolution with Elite Chaotic Local Search.

Area of Science:

• Computational intelligence
• Optimization algorithms
• Evolutionary computation

Background:

• Differential evolution (DE) is an effective evolutionary algorithm for engineering problems.
• Standard DE may require enhanced search capabilities for complex optimization tasks.
• Improving exploitation and robustness in DE is crucial for better solution quality.

Purpose of the Study:

• To present an enhanced differential evolution algorithm named DEECL.
• To improve the search ability and solution quality of DE for complex problems.
• To introduce a novel approach combining elite chaotic local search and parameter adaptation.

Main Methods:

• Developed DEECL by integrating a chaotic search strategy guided by elite individuals.
• Implemented a parameter adaptation mechanism to boost algorithm robustness.
• Conducted experiments on a suite of classical, well-known test functions.

Main Results:

• DEECL demonstrated a strong ability to exploit search space using heuristic information from elite solutions.
• The parameter adaptation mechanism enhanced the algorithm's robustness across different problems.
• Experimental results indicate DEECL is highly competitive on most tested functions.

Conclusions:

• DEECL offers a significant improvement over standard DE for complex optimization problems.
• The combination of elite chaotic local search and adaptive parameters is effective.
• DEECL shows promising performance and competitiveness in solving challenging optimization tasks.