A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bio-inspired Computing

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems.
• Nature-inspired algorithms offer unique approaches to computational challenges.
• Existing algorithms may struggle with balancing exploration and exploitation phases.

Purpose of the Study:

• Introduce a novel bio-inspired metaheuristic algorithm, the Walrus Optimization Algorithm (WaOA).
• Evaluate the performance of WaOA on a comprehensive set of benchmark functions.
• Assess the applicability of WaOA to real-world engineering and optimization problems.

Main Methods:

• WaOA is designed based on walrus behaviors: feeding, migrating, escaping predators, and fighting.
• The algorithm's steps are mathematically modeled into exploration, migration, and exploitation phases.
• Performance is evaluated using 68 standard benchmark functions, including CEC 2015 and CEC 2017 test suites, and compared against ten established metaheuristic algorithms.

Main Results:

• WaOA demonstrates strong exploitation capabilities on unimodal functions and exploration capabilities on multimodal functions.
• The algorithm effectively balances exploration and exploitation, achieving superior results on most benchmark functions.
• WaOA shows significant effectiveness in solving engineering design issues and real-world optimization problems.

Conclusions:

• The Walrus Optimization Algorithm (WaOA) presents a competitive and superior performance compared to other metaheuristic algorithms.
• WaOA's ability to balance exploration and exploitation makes it highly effective for diverse optimization applications.
• The algorithm's successful application to real-world problems highlights its practical utility.