New enhanced artificial bee colony (JA-ABC5) algorithm with application for reactive power optimization.

Area of Science:

• Artificial Intelligence
• Optimization Algorithms
• Computational Intelligence

Background:

• The standard artificial bee colony (ABC) algorithm requires a balance between exploration and exploitation for optimal performance.
• Existing ABC algorithms may face challenges in achieving rapid convergence and finding global optima.

Purpose of the Study:

• To propose a novel modified ABC algorithm, JA-ABC5, designed to improve convergence speed and global optimum attainment.
• To enhance the exploration-exploitation balance within the ABC framework.

Main Methods:

• Introduction of new stages in the early phases of the algorithm to bolster the exploitation process.
• Modification of mutation equations within the employed and onlooker-bee phases to achieve a better balance between exploration and exploitation.
• Comprehensive performance evaluation using 27 standard benchmark functions and the reactive power optimization problem.

Main Results:

• The JA-ABC5 algorithm demonstrated superior performance compared to other algorithms.
• Significant improvements were observed in convergence speed.
• Enhanced ability to achieve the global optimum was confirmed.

Conclusions:

• The proposed JA-ABC5 algorithm effectively balances exploration and exploitation.
• JA-ABC5 offers a promising advancement for optimization tasks requiring fast convergence and global optimum solutions.
• The modified ABC algorithm shows strong potential for complex optimization problems such as reactive power optimization.