Serval Optimization Algorithm: A New Bio-Inspired Approach for Solving Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems.
• Existing algorithms face challenges in balancing exploration and exploitation phases.
• Nature-inspired strategies offer novel approaches to algorithm design.

Purpose of the Study:

• Introduce the Serval Optimization Algorithm (SOA), a novel metaheuristic.
• Model SOA's exploration and exploitation phases based on serval hunting.
• Evaluate SOA's performance on benchmark functions and real-world problems.

Main Methods:

• Developed the Serval Optimization Algorithm (SOA) inspired by serval hunting.
• Mathematically modeled SOA's exploration and exploitation mechanisms.
• Tested SOA on CEC 2017, CEC 2019, and CEC 2011 benchmark suites.
• Compared SOA against twelve established metaheuristic algorithms.
• Applied SOA to four engineering design challenges.

Main Results:

• SOA demonstrated superior performance on most benchmark functions.
• The algorithm effectively balanced exploration and exploitation.
• SOA outperformed competing metaheuristic algorithms in tested scenarios.
• Successful application of SOA to real-world engineering design problems.

Conclusions:

• The Serval Optimization Algorithm (SOA) is a highly efficient and competitive metaheuristic.
• SOA's nature-inspired approach provides effective solutions for complex optimization.
• The algorithm shows significant potential for diverse real-world applications.