CGJO: a novel complex-valued encoding golden jackal optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• The Golden Jackal Optimization (GJO) algorithm, inspired by jackal behavior, suffers from slow convergence, low accuracy, and inefficient exploration/exploitation.
• Existing optimization methods often struggle with premature convergence and limited solution efficiency.

Purpose of the Study:

• To propose a novel Complex-Valued Encoding Golden Jackal Optimization (CGJO) algorithm.
• To enhance global detection ability and solution accuracy in optimization problems.
• To improve the efficiency and robustness of the GJO algorithm for function optimization and engineering design.

Main Methods:

• Developed a complex-valued encoding strategy using a dual-diploid organization for golden jackals.
• Converted dual-dimensional encoding to a single-dimensional manifestation region to increase population diversity.
• Applied CGJO to the CEC 2022 test suite and six real-world engineering design problems.

Main Results:

• CGJO demonstrated increased population diversity, reduced search stagnation, and expanded exploration capabilities.
• The algorithm achieved a balance between global exploration and local exploitation, enhancing computational precision.
• Experimental results showed CGJO outperformed recently published, highly cited, and high-performing optimization algorithms.

Conclusions:

• CGJO offers significant improvements over existing algorithms, including faster convergence, higher computational precision, and enhanced stability and robustness.
• The complex-valued encoding strategy effectively addresses the limitations of the original GJO.
• CGJO presents a superior and reliable approach for complex optimization tasks and engineering designs.