Improved quick artificial bee colony (iqABC) algorithm for global optimization

Aslan, Selçuk; Badem, Hasan; Karaboğa, Derviş

doi:10.1007/s00500-019-03858-y

Cited by 53 publications

(40 citation statements)

References 37 publications

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“…B 05 Identify the worst areas of soil with (3), as shown in II. C 06 Update memory with (4) and (5), as shown in II. D 07 Optimize soil with (6), (7), (8), and (9), as shown in II.…”

Section: Algorithm 1: Ffmentioning

confidence: 99%

“…Popular swarm intelligence evolutionary algorithms include ant colony optimization (ACO) algorithm, particle swarm optimization (PSO) algorithm, bacterial foraging optimization (BFO) algorithm, shuffled frog-leaping algorithm (SFLA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), firework algorithm (FWA), gray wolf optimizer (GWO), and crow search algorithm (CSA). The ACO algorithm simulates the behavior of an ant colony in seeking the shortest path during the foraging process [1]; the PSO algorithm simulates the foraging behavior of birds [2]; the BFO algorithm simulates the foraging behavior of Escherichia coli in human intestines [3]; the SFLA simulates the behavior of frogs looking for stones in water to jump and find a place with substantial food [4]; the ABC algorithm simulates the behavior of bees in identifying the largest nectar source through a complete nectar collection mechanism [5]; the FA simulates the behavior of fireflies communicating with others through flashing [6]; the FWA simulates the behavior of firework explosion generating sparks and continuing to explode and split [7]; the GWO simulates the behavior of a gray wolf during preying [8]; the CSA simulates the behavior of crows when hiding and searching for food in different seasons [9].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Farmland Fertility Algorithm for Global Function Optimization

Wang

Chen

2020

IEEE Access

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This paper proposes an improved farmland fertility (IFF) algorithm to increase the convergence rate and precision of the farmland fertility algorithm. A search mode that combines subspace and full space is proposed. The two modes are automatically converted in accordance with the current learning level of the population. Such hybrid search balances the algorithm exploration and exploitation capabilities. The global memory capacity with a fixed size is processed self-adaptively to make its size adaptively change with the iterative process. A mechanism of soil fusion based on neighbor memory learning is also proposed to expand the search range of the current population and ensure population diversity. On the basis of the cosine similarity between the population and the current optimal individual, the area to which the individual belongs is periodically redivided, and the computing resources are reasonably allocated. The results of experiments on the CEC2013 test suite indicate that IFF has evident advantages over seven excellent algorithms in terms of convergence rate and precision.

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“…B 05 Identify the worst areas of soil with (3), as shown in II. C 06 Update memory with (4) and (5), as shown in II. D 07 Optimize soil with (6), (7), (8), and (9), as shown in II.…”

Section: Algorithm 1: Ffmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Farmland Fertility Algorithm for Global Function Optimization

Wang

Chen

2020

IEEE Access

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“…Although their evaluation results show that the better local search ability in qABC algorithm, it suffers from the premature convergence problem. Thus, iqABC [40] was recently proposed by introducing different search schemas by which the qualities of the final solutions and convergence speeds are enhanced.…”

Section: Swarm Intelligence Algorithms Based On Insects Behaviorsmentioning

confidence: 99%

CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

Zamani

Nadimi-Shahraki

Gandomi

2019

Applied Soft Computing

118

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In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design.In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for largescale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design.

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“…Kumar and Mishra proposed a covariance-based guided ABC (CABC) [20], in which the covariance information was embedded to accelerate the convergence. Aslan et al designed an improved qABC (iqABC) [21] to balance the search ability. Ji et al proposed a scale-free ABC (SFABC) [22], in which the topology information of a scale-free network introduced into ABC.…”

Section: Introductionmentioning

confidence: 99%

A Tristage Adaptive Biased Learning for Artificial Bee Colony

Jiang

Lin

et al. 2021

Discrete Dynamics in Nature and Society

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In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs.

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Improved quick artificial bee colony (iqABC) algorithm for global optimization

Cited by 53 publications

References 37 publications

An Improved Farmland Fertility Algorithm for Global Function Optimization

An Improved Farmland Fertility Algorithm for Global Function Optimization

CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems

A Tristage Adaptive Biased Learning for Artificial Bee Colony

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