Lbest artificial bee colony using structured swarm

Saxena, Shraddha; Sharma, Kapil; Shiwani, Savita; Sharma, Harish

doi:10.1109/iadcc.2014.6779524

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…In late stage of optimization process, local search becomes more important than global search, because better local search ability means higher speed of convergence and leads to more accurate result. In addition, although a few different search equations are proposed in [17,[36][37][38][39], each algorithm only supplies a better solution for some specific problems than others. Therefore, it is very necessary to seek for a well improved method to be compatible for different problems.…”

Section: A New Search Equation Based On Factor Librarymentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance

Cai

et al. 2018

Mathematical Problems in Engineering

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The artificial bee colony (ABC) algorithm is a relatively new optimization technique for simulating the honey bee swarms foraging behavior. Due to its simplicity and effectiveness, it has attracted much attention in recent years. However, ABC search equation is good at global search but poor at local search. Some different search equations are developed to tackle this problem, while there is no particular algorithm to substantially attain the best solution for all optimization problems. Therefore, we proposed an improved ABC with a new search equation, which incorporates the global search factor based on the optimization problem dimension and the local search factor based on the factor library (FL). Furthermore, aimed at preventing the algorithm from falling into local optima, dynamic search balance strategy is proposed and applied to replace the scout bee procedure in ABC. Thus, a hybrid, fast, and enhanced algorithm, HFEABC, is presented. In order to verify its effectiveness, some comprehensive tests among HFEABC and ABC and its variants are conducted on 21 basic benchmark functions and 20 complicated functions from CEC 2017. The experimental results show HFEABC offers better compatibility for different problems than ABC and some of its variants. The HFEABC performance is very competitive.

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Section: A New Search Equation Based On Factor Librarymentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance

Cai

et al. 2018

Mathematical Problems in Engineering

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“…Different versions of ABC algorithm have been proposed like artificial bee colony algorithm with mutation [9], ABC with crossover, ABC with SPV [10], ABC with crossover and mutation [11] and many different versions have been proposed. In paper [2], dynamically division of bees into subgroup and the searching process is performed on these generated subgroups. In paper [3], Karaboga defines the modified algorithm for solving real parameter optimization problems and comparative study on different evolutionary algorithms like particle swarm optimization (PSO), ABC algorithm, Differential evolution (DE), Ant colony algorithm (ACO) etc and performance derived on different benchmark functions, are discussed on paper [5,7].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Artificial Bee Colony Algorithm with SPV for Travelling Salesman Problem

Shrivastava¹,

Gupta

Swami³

2015

2015 International Conference on Computing Communication Control and Automation

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Artificial Bee Colony (ABC) Algorithm is an optimization algorithm used to find out the global optima. In ABC, each bee stores the information of feasible solution or candidate solution and stochastically modifies this over time, based on the information provided by neighboring bees, it speculative modifies over time and based on the best solution found by the bee itself.. In this proposed work, enhanced ABC algorithm with SPV for travelling salesman problem is used. In this modified bee colony algorithm, additional phase in the form of mutation is used and SPV rule is used in this work for improving local search. After modification, proposed algorithm is implemented on standard travelling salesman problem for checking the efficiency of proposed work. The experimental results are compared with ABC algorithm and ABC with SPV algorithm.

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“…And the algorithm dynamically adjusts the balance between the abilities of global search and local development. Saxena et al (2014) learns from the local optimal concept in the PSO algorithm, so that the local optimal information of the honey source is involved in the composition of the offspring candidate solution, which improves the search ability of the algorithm in solution space. In order to reduce the probability of falling into local optimum due to the decrease of the population diversity, Xiang and An (2013) proposed the MABC algorithm, which uses fixed parameter P instead of the selection probability in the roulette strategy to improve the algorithm performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-strategy artificial bee colony based on multiple population for coverage optimisation

Sun

Wang

Xie

2018

IJWMC

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In order to overcome the shortcomings of weak local search ability and slow convergence speed for the standard artificial bee colony algorithm, this paper proposes an improved multi-strategy artificial bee colony algorithm based on multiple populations (IMSABC). Firstly, the employed bees are randomly divided into three subgroups, corresponding to three evolutionary strategies. If the candidate solution obtained from searching is inferior to the current honey source, the bee is randomly assigned to other subgroups and the search strategy is changed. In this way, it not only facilitates the information exchange between populations, but also balances the global search and local development capabilities of the algorithm since the three search strategies have different characteristics. Secondly, by imitating the particle swarm algorithm, the search strategy of the following bees is improved by using the abundant information contained in the current global optimal honey source and random neighbour honey source. The simulation results of twelve benchmark test functions and 28 CEC2013 functions show that the performance of this algorithm has significant advantages compared with many similar improved algorithms. In order to improve the unreasonable distribution of sensor nodes and improve the network coverage, the above algorithm is applied to optimise the coverage of wireless sensor networks and achieve better optimisation effect.

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Lbest artificial bee colony using structured swarm

Cited by 8 publications

References 13 publications

An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance

An Improved Artificial Bee Colony Algorithm Based on Factor Library and Dynamic Search Balance

Enhanced Artificial Bee Colony Algorithm with SPV for Travelling Salesman Problem

Multi-strategy artificial bee colony based on multiple population for coverage optimisation

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