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Enhancing artificial bee colony algorithm with self-adaptive searching strategy and artificial immune network

Tinggui Chen¹, Renbin Xiao²

¹College of Computer Science & Information Engineering, Zhejiang Gongshang University, Zhejiang Province, Hangzhou 310018, China.

Thescientificworldjournal

|April 29, 2014

Summary

The enhanced Artificial Bee Colony (ABC) algorithm, EABC, overcomes limitations of the basic ABC by incorporating self-adaptive strategies and artificial immune networks. EABC demonstrates superior performance on benchmark functions compared to other algorithms.

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Area of Science:

Computational intelligence
Optimization algorithms
Swarm intelligence

Background:

The Artificial Bee Colony (ABC) algorithm is inspired by honey bee foraging behavior.
While effective, ABC can be trapped in local optima for complex functions.
Existing algorithms like Genetic Algorithm (GA), Artificial Colony Optimization (ACO), and Particle Swarm Optimization (PSO) have limitations.

Purpose of the Study:

To address the limitations of the standard ABC algorithm.
To enhance the exploration and exploitation capabilities of the ABC algorithm.
To propose a novel enhanced ABC algorithm (EABC).

Main Methods:

Introduction of a self-adaptive searching strategy.
Integration of artificial immune network operators.
Testing on a suite of unimodal and multimodal benchmark functions.

Main Results:

The proposed EABC algorithm shows improved performance over the basic ABC.
EABC outperforms ACO and PSO on most tested benchmark functions.
The enhanced strategies effectively improve exploitation and exploration.

Conclusions:

The EABC algorithm offers a significant improvement over the standard ABC.
EABC provides a robust solution for complex optimization problems.
The integration of novel operators enhances the algorithm's effectiveness.