A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems.

Area of Science:

• Artificial Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• The artificial bee colony (ABC) algorithm is a popular swarm intelligence technique inspired by honeybee foraging behavior.
• Existing ABC algorithms face challenges in convergence speed, solution accuracy, and balancing exploration-exploitation.
• Enhancements are needed to improve the efficiency and effectiveness of ABC for complex optimization tasks.

Purpose of the Study:

• To propose a novel self-adaptive hybrid enhanced artificial bee colony (ABC) algorithm.
• To improve the convergence ability and search speed of the ABC algorithm.
• To effectively control the balance between exploration and exploitation in optimization.

Main Methods:

• Development of a self-adaptive hybrid enhanced ABC algorithm.
• Implementation and evaluation using numerical optimization problems from the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite.
• Comparative analysis against standard ABC, best-so-far ABC (BsfABC), and incremental ABC (IABC) algorithms.

Main Results:

• Experimental results demonstrate the comparative performance of the proposed ABC algorithm against standard, BsfABC, and IABC.
• The proposed algorithm shows competitive performance on benchmark problems with varying dimensions (10, 30, and 50).
• The self-adaptive hybrid enhancement contributes to improved optimization capabilities.

Conclusions:

• The proposed self-adaptive hybrid enhanced ABC algorithm is a competitive alternative to existing state-of-the-art modified ABC algorithms.
• The enhanced ABC algorithm effectively addresses limitations in convergence and search speed.
• The study validates the proposed algorithm's efficacy on challenging benchmark optimization problems.