An Enhanced Hunger Games Search Optimization with Application to Constrained Engineering Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• The Hunger Games Search (HGS) is a gradient-free, population-based optimizer inspired by social animal foraging.
• HGS faces limitations such as inadequate diversity, premature convergence, and local optima susceptibility.
• Enhancing HGS is crucial for broader applicability in complex optimization tasks.

Purpose of the Study:

• To introduce and evaluate two novel adaptive strategies to improve the original HGS algorithm.
• To develop an enhanced algorithm, RLHGS, by integrating Logarithmic Spiral with Opposition-based Learning (LS-OBL) and dynamic Rosenbrock Method (RM).
• To assess the performance of RLHGS against state-of-the-art algorithms on benchmark functions and real-world engineering problems.

Main Methods:

• Developed the LS-OBL strategy to reduce search space and maintain population diversity, enhancing exploration.
• Incorporated the dynamic Rosenbrock Method (RM) to adjust search direction and step size, aiding escape from local optima.
• Combined LS-OBL and RM to create the RLHGS algorithm for improved convergence and accuracy.

Main Results:

• Experiments confirmed that LS-OBL and RM significantly enhance HGS capabilities.
• RLHGS outperformed eight state-of-the-art algorithms on 23 benchmark functions and the CEC2020 test suite.
• RLHGS effectively solved four constrained real-world engineering optimization problems, demonstrating practical utility.

Conclusions:

• The proposed RLHGS algorithm, integrating LS-OBL and RM, offers superior optimization performance.
• RLHGS effectively addresses limitations of the original HGS, showing enhanced diversity, convergence, and global search capabilities.
• RLHGS proves to be a highly effective and efficient optimization method for both theoretical and practical engineering challenges.