An Improved Artificial Bee Colony Algorithm Applied to Engineering Optimization Problems (original) (raw)
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Artificial Bee Colony (ABC) Algorithm for Engineering Optimization Problems and its advantages
In this work, the performance of the Artificial Bee Colony (ABC) algorithm in engineering optimization problems is compared against those of other methods reported in the literature. The classic spring design optimization problem, and truss optimization on size and shape with frequency constraint problems were chosen for the numerical experiments. It is well known that algorithm performance is problem dependent. Taking advantage of its flexibility, and based on related works, some modifications were implemented in the ABC algorithm. The results presented here indicate that the ABC algorithm is an effective global optimizer with relative high computational cost. However, its performance is comparable to the state of the art metaheuristics algorithms. Therefore, the applicability of the ABC algorithm in engineering optimization problems is compromised with its cost-benefit function, by weighing the advantages against the disadvantages of its characteristic features.
A ranking-based adaptive artificial bee colony algorithm for global numerical optimization
Information Sciences, 2017
The artificial bee colony (ABC) algorithm is a powerful population-based metaheuristic for global numerical optimization and has been shown to compete with other swarm-based algorithms. However, ABC suffers from a slow convergence speed. To address this issue, the natural phenomenon in which good individuals always have good genes and thus should have more opportunities to generate offspring is the inspiration for this paper. We propose a ranking-based adaptive ABC algorithm (ARABC). Specifically, in ARABC, food sources are selected by bees to search, and the parent food sources used in the solution search equation are all chosen based on their rankings. The higher a food source is ranked, the more opportunities it will have to be selected. Moreover, the selection probability of the food source is based on the corresponding ranking, which is adaptively adjusted according to the status of the population evolution. To evaluate the performance of ARABC, we compare ARABC with other ABC variants and state-of-the-art differential evolution and particle swarm optimization algorithms based on a number of benchmark functions. The experimental results show that ARABC is significantly better than the algorithms to which it was compared.
An enhanced artificial bee colony algorithm based on fitness weighted search strategy
Automatika
Artificial Bee Colony (ABC) algorithm is a meta-heuristic algorithm, is inspired by the bee's food search behaviour based on swarm intelligence. Successful applications were performed on many optimization problems using this algorithm rising in popularity over the past few years. The update mechanism of the ABC algorithm, despite the fact that its exploration is good, faces the problem of convergence performance. For solving convergence problem of ABC Algorithm An Artificial Bee Colony Algorithm Based Fitness Weighted Search (ABCFWS) algorithm proposed in this paper. In this approach, an intelligent search space is proposed instead of the random search space of the ABC algorithm. In this method, the fitness values of the food source and the selected neighbour food source are taken as weights and a more balanced search space was found in the direction of the food source with better fitness value. The proposed method has been applied to 28 unconstrained numerical optimization test problems with different characteristics and the results were compared with the ABC algorithm variations. The results show that the proposed method is successful and competitive.
Enhanced artificial bee colony optimization
International Journal of …, 2009
An enhanced Artificial Bee Colony (ABC) optimization algorithm, which is called the Interactive Artificial Bee Colony (IABC) optimization, for numerical optimization problems, is proposed in this paper. The onlooker bee is designed to move straightly to the picked coordinate indicated by the employed bee and evaluates the fitness values near it in the original Artificial Bee Colony algorithm in order to reduce the computational complexity. Hence, the exploration capacity of the ABC is constrained in a zone. Based on the framework of the ABC, the IABC introduces the concept of universal gravitation into the consideration of the affection between employed bees and the onlooker bees. By assigning different values of the control parameter, the universal gravitation should be involved for the IABC when there are various quantities of employed bees and the single onlooker bee. Therefore, the exploration ability is redeemed about on average in the IABC. Five benchmark functions are simulated in the experiments in order to compare the accuracy/quality of the IABC, the ABC and the PSO. The experimental results manifest the superiority in accuracy of the proposed IABC to other methods.
A Comparative Analysis of Selection Schemes in the Artificial Bee Colony Algorithm
Computación y Sistemas, 2016
The Artificial Bee Colony (ABC) algorithm is a popular swarm based algorithm inspired by the intelligent foraging behavior of honey bees. In the past, many swarm intelligence based techniques were introduced and proved their effective performance in solving various optimization problems. The exploitation of food sources is performed by onlooker bees in accordance with a proportional selection scheme that can be further modified to avoid such shortcomings as population diversity and premature convergence. In this paper, different selection schemes, namely, tournament selection, truncation selection, disruptive selection, linear dynamic scaling, linear ranking, sigma truncation, and exponential ranking have been used to analyze the performance of the ABC algorithm by testing on standard benchmark functions. From the simulation results, the schemes other than the standard ABC prove their efficient performance.
International Journal of Applied Metaheuristic Computing, 2013
Nowadays computers are used to solve a variety and multitude of complex problems facing in every sphere of peoples’ life. However, many of the problems are intractable in nature exact algorithm might need centuries to manage with formidable challenges. In such cases heuristic or in a broader sense meta-heuristic algorithms that find an approximate solution but have acceptable time and space complexity play indispensable role. In this article, the authors present a state-of-the-art review on meta-heuristic algorithm popularly known as artificial bee colony (ABC) inspired by honey bees. Moreover, the ABC algorithm for solving single and multi-objective optimization problems have been studied. A few potential application areas of ABC are highlighted as an end note of this article.
An enhanced hybridized artificial bee colony algorithm for optimization problems
IAES International Journal of Artificial Intelligence (IJ-AI), 2019
Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.
A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems
Biosystems, 2015
The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively. 2015 Elsevier Ireland Ltd. All rights reserved.
Knowledge-Based Artificial Bee Colony Algorithm for Optimization Problems
This paper presents a cultural artificial bee colony algorithm to modify the artificial bee colony (ABC). The normative and situational knowledge inherent in the cultural algorithm is utilized to guide the step size as well as the direction of evolution of ABC at different arrangements. This was done in order to combat the disparity between exploration and exploitation associated with the standard ABC, which results in poor convergence and optimization inefficiency. Four variants of Cultural Artificial Bee Colony Algorithm (CABCA) are accomplished in MATLAB/Simulink program using different configurations of cultural knowledge. A total of 20 standards applied mathematical optimization benchmark functions (Ackley, Michalewicz, Quartic, Sphere etc) are employed to evaluate the performance, and it was found that all the four variants of CABCA outperformed the standard ABC. The superiority of CABCA variants over ABC justifies the essence of knowledge introduction in the belief space for self-adaptation.
Bee Colony Optimization Overview
The Bee Colony Optimization (BCO) meta-heuristic belongs to the class of Nature-Inspired Algorithms. This technique uses an analogy between the way in which bees in nature search for a food, and the way in which optimization algorithms search for an optimum in combinatorial optimization problems. Artificial bees represent agents, which collaboratively solve complex combinatorial optimization problem. The chapter presents a description of the algorithm, classification and analysis of the results achieved using Bee Colony Optimization (BCO) to model complex engineering and management processes.