A hybrid artificial bee colony optimizer by combining with life-cycle, Powell’s search and crossover

Ma, Lianbo; Hu, Kun; Zhu, Yunlong; Chen, Hanning

doi:10.1016/j.amc.2014.11.104

Cited by 23 publications

(21 citation statements)

References 33 publications

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“…x 22 show significant consistency, and corresponding feeding amount i.e., x 1 , ..., x 11 remain almost the same. This also indicates that the proposed method obtains satisfactory convergence of solutions.…”

Section: Results On Bc_8mentioning

confidence: 84%

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A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

Huang

Zhang

et al. 2017

Applied Soft Computing

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This paper proposes a novel multi-objective root system growth optimizer (MORSGO) for the copper strip burdening optimization. The MORSGO aims to handle multi-objective problems with satisfactory convergence and diversity via implementing adaptive root growth operators with a pool of multi-objective search rules and strategies. Specifically, the single-objective root growth operators including branching, regrowing and auxin-based tropisms are deliberately designed. They have merits of appropriately balancing exploring & exploiting and self-adaptively varying population size to reduce redundant computation. The effective multi-objective strategies including the fast non-dominated sorting and the farthest-candidate selection are developed for saving and retrieving the Pareto optimal solutions with remarkable approximation as well as uniform spread of Pareto-optimal solutions. With comprehensive evaluation against a suit of benchmark functions, the MORSGO is verified experimentally to be superior or at least comparable to its competitors in terms of the IGD and HV metrics. The MORSGO is then validated to solve the real-world copper strip burdening optimization with different elements. Computation results verifies the potential and effectiveness of the MORSGO to resolve complex industrial process optimization.

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Section: Results On Bc_8mentioning

confidence: 84%

“…This self-adaptive population variation indicates that each individual can dynamically switch its state from branching, to death throughout the growing process. As a result, the population size varies dynamically according to the local fitness landscape [22].…”

mentioning

confidence: 99%

A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

Huang

Zhang

et al. 2017

Applied Soft Computing

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“…In this algorithm, employed bees and onlooker bees use the idea of neighborhood search to find best nectar source. ABC algorithm is proved to perform well on many optimization instances [23,24]. In this paper, we combined the framework of MOEA/D and the neighborhood search of ABC and proposed a new approach, which is named MOABC/D.…”

Section: Multiobjective Optimization and Moabc/d Algorithmmentioning

confidence: 99%

A Novel Algorithm to Scheduling Optimization of Melting-Casting Process in Copper Alloy Strip Production

Yan

Zhang

Guo

et al. 2015

Discrete Dynamics in Nature and Society

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Melting-casting is the first process in copper alloy strip production. The schedule scheme on this process affects the subsequent processes greatly. In this paper, we build the multiobjective model of melting-casting scheduling problem, which considers minimizing the makespan and total weighted earliness and tardiness penalties comprehensively. A novel algorithm, which we called Multiobjective Artificial Bee Colony/Decomposition (MOABC/D) algorithm, is proposed to solve this model. The algorithm combines the framework of Multiobjective Evolutionary Algorithm/Decomposition (MOEA/D) and the neighborhood search strategy of Artificial Bee Colony algorithm. The results on instances show that the proposed MOABC/D algorithm outperforms the other two comparison algorithms both on the distributions of the Pareto front and the priority in the optimal selection results.

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“…Furthermore, it has been compared thoroughly with other state of the state-of-the-art intelligent algorithms, such as DE, PSO, GA and ES. However, ABC algorithm suffers from the following drawbacks [7][8][9][10][11]: (1) Narrow search zone: the learning mechanism based on the random selection of neighbor and dimensions, causes the individual's searching restricted in a narrow space. (2) Slow convergence: the method of perturbation used in the classical ABC limits the information exchange of each individual in a random dimension, causing the slow convergence rate.…”

Section: Introductionmentioning

confidence: 99%

A modified Artificial Bee Colony optimizer by comprehensive learning and Powell' search

Liu

Shao

Liu

et al. 2015

2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)

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T In order to improve the algorithmic ability of balancing the exploration and exploitation tradeoff, a modified Artificial Bee Colony optimizer (MABC) is proposed by combining Powell's search and comprehensive learning using PSO-based search equation strategy. With comprehensive learning, the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the Powell's search enables the bees deeply exploit around the promising area, which provides a proper balance between exploration and exploitation. The experimental results on a set of benchmarks demonstrated the effectiveness of the proposed algorithm.

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A hybrid artificial bee colony optimizer by combining with life-cycle, Powell’s search and crossover

Cited by 23 publications

References 33 publications

A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

A novel evolutionary root system growth algorithm for solving multi-objective optimization problems

A Novel Algorithm to Scheduling Optimization of Melting-Casting Process in Copper Alloy Strip Production

A modified Artificial Bee Colony optimizer by comprehensive learning and Powell' search

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