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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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

Vanisree Chandran¹, Prabhujit Mohapatra¹

¹Department of Mathematics, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.

|May 23, 2024

Summary

A new Quasi-Oppositional Chaotic Tunicate Swarm Algorithm (QOCTSA) enhances optimization by combining Quasi-Oppositional Based Learning and Chaotic Local Search. This novel approach improves convergence accuracy and exploration for complex engineering problems.

Keywords:

Chaotic maps Engineering design problems Meta-heuristic algorithms Quasi-oppositional based learning (QOBL)Tunicate swarm algorithm (TSA)

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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.