A novel multi-strategy ameliorated quasi-oppositional chaotic tunicate swarm algorithm for global optimization and...

Area of Science:

• Optimization Algorithms
• Computational Intelligence
• Bio-inspired Computing

Background:

• Meta-heuristic algorithms are crucial for complex optimization problems.
• Existing algorithms like the Tunicate Swarm Algorithm (TSA) face limitations such as premature convergence and local optima entrapment.
• Enhancing meta-heuristics with evolutionary techniques is vital for addressing modern engineering challenges.

Purpose of the Study:

• To improve the efficiency and performance of the Tunicate Swarm Algorithm (TSA).
• To introduce a novel enhanced variant, the Quasi-Oppositional Chaotic TSA (QOCTSA), to overcome TSA's limitations.
• To effectively balance exploration and exploitation capabilities in optimization.

Main Methods:

• Developed the Quasi-Oppositional Chaotic TSA (QOCTSA) by integrating Quasi-Oppositional Based Learning (QOBL) and Chaotic Local Search (CLS) into the TSA.
• QOBL enhances convergence accuracy and exploration rate.
• CLS, utilizing ten chaotic maps, improves exploitation by refining local search.

Main Results:

• QOCTSA demonstrated superior performance compared to the original TSA.
• The enhanced algorithm exhibited a faster convergence rate across various test functions.
• Statistical tests confirmed QOCTSA's advantage over other competing algorithms on real-world engineering problems.

Conclusions:

• QOCTSA effectively addresses the limitations of TSA, particularly premature convergence and local optima.
• The integration of QOBL and CLS significantly boosts optimization accuracy and maintains diversification.
• QOCTSA represents a promising advancement for solving complex optimization tasks in engineering applications.