scholarly journals Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wang Chun-Feng ◽  
Liu Kui ◽  
Shen Pei-Ping
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Particle Swarm ◽  
Population Based ◽  
Initial Population ◽  
Bee Colony ◽  
Search Mechanism ◽  
Point Set ◽  
Searching Ability ◽  
Good Point Set

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.

scholarly journals An Artificial Bee Colony Algorithm with Random Location Updating

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lijun Sun ◽  
Tianfei Chen ◽  
Qiuwen Zhang
Keyword(s):  
Information Exchange ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Selection Strategy ◽  
Initial Population ◽  
Search Range ◽  
Bee Colony ◽  
Random Location ◽  
Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension.

Research on Cellular Artificial Bee Colony Algorithm and its Computational Experiments

2013 ◽  
Vol 284-287 ◽  
pp. 3168-3172
Author(s):  
Ren Bin Xiao ◽  
Ying Cong Wang
Keyword(s):  
Population Structure ◽  
Cellular Automata ◽  
Selection Pressure ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Exploration And Exploitation ◽  
Fixed Position ◽  
Bee Colony ◽  
Search Mechanism ◽  
The Relationship

It is the research hotspot for evolutionary algorithms to solve the contradiction between exploration and exploitation. Cellular artificial bee colony (CABC) algorithm is proposed by combining cellular automata with artificial bee colony algorithm from the perspective of the neighborhood in this paper. Each bee in the population structure defined in CABC has a fixed position and can only interact with bees in its neighborhood. The overlap between neighborhoods of different bees may make a bee an employed bee in one neighborhood and an onlooker bee in another neighborhood and vice versa, which increases the diversity of the population. The neighborhood and evolutionary rule help to control the selection pressure effectively, and the improved search mechanism in artificial bee colony algorithm is proposed to enhance the local search ability. The experimental results tested on four benchmark functions show that CABC can further balance the relationship between exploration and exploitation when compared with three ABC-based algorithms.

scholarly journals Artificial Bee Colony Algorithm with Time-Varying Strategy

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Quande Qin ◽  
Shi Cheng ◽  
Qingyu Zhang ◽  
Li Li ◽  
Yuhui Shi
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Experimental Studies ◽  
Population Based ◽  
Time Varying ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Linear And Nonlinear ◽  
Proper Balance

Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.

Dynamic Function Optimization by Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3562-3566 ◽  
Author(s):  
Shuo Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Greedy Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Dynamic Function

In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamic function in time.

Scheduling of Cellular Manufacturing With Flexible Routes Intercell Moves for Carbon Reduction in a Network Environment

2019 ◽  
Author(s):  
Jialiang Liu ◽  
Qiong Liu ◽  
Chenxin Xu ◽  
Zhaorui Dong ◽  
Mengbang Zou
Keyword(s):  
Carbon Emissions ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Cellular Manufacturing ◽  
Convergence Speed ◽  
Total Carbon ◽  
Network Environment ◽  
Bee Colony ◽  
Scheduling Model ◽  
Searching Ability

Abstract In order to rapidly share manufacturing resources among enterprises in a network environment, reduce carbon emissions and production costs, scheduling of cellular manufacturing with intercell moves is studied. Previous researches on cellular manufacturing with intercell moves either supposed that a part can only move between two cells at most one time or supposed that intercell moves of parts were on fixed paths. However, there might be several manufacturing cells with the same processing function or several same machines in different cells in a network environment. Intercell moves of parts might have flexible routes. To make the cellular manufacturing with intercell moves in a network environment, a scheduling model aiming at minimizing total carbon emissions, makespan and total costs is proposed for intercell moves with flexible routes and no restrictions on the number of intercell moves. An improved artificial bee colony algorithm (ABC) is proposed to solve the scheduling model. In order to improve searching ability of ABC, neighborhood search with an adaptive stepsize mechanism is proposed in leader bee phase and onlooker phase of the algorithm. A binary tournament selection method is designed to improve convergence speed in the onlooker bee phase. A case study is used to verify the proposed model and algorithm. The results show that improved algorithm has better performance on convergence speed and searching ability than that of original artificial bee colony algorithm.

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