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A ranking-based adaptive artificial bee colony algorithm for global numerical optimization

INFORMATION SCIENCES 2017年 417卷 169-185页

作者： Cui, Laizhong Li, Genghui Wang, Xizhao Lin, Qiuzhen Chen, Jianyong Lu, Nan Lu, Jian Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen Peoples R China

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. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Artificial bee colony algorithm Adaptive ranking selection global numerical optimization

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Multi-operator based biogeography based optimization with mutation for global numerical optimization

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2012年第9期64卷 2833-2844页

作者： Li, Xiangtao Yin, Minghao NE Normal Univ Coll Comp Sci Changchun 130117 Peoples R China

Biogeography based optimization (BBO) is a new evolutionary optimization based on the science of biogeography for global optimization. We propose two extensions to BBO. First, we propose a new migration operation based multi-parent crossover called multi-parent migration model, which is a generalization of the standard BBO migration operator. The new migration model can satisfy a balance of exploration and exploitation. Second, the Gaussian mutation operator is integrated into multi-operator biogeography based optimization (MOBBO) to enhance its exploration ability and to improve the diversity of population. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. Simulation results and comparisons demonstrate the proposed MOBBO algorithm based multi-parent crossover model is better, or at least comparable to, the BBO, PBBO and evolutionary algorithms from literature when considering the quality of the solutions obtained. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Biogeography based optimization Multi-parent crossover operator global numerical optimization Exploration Exploitation

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Drone Squadron optimization: a novel self-adaptive algorithm for global numerical optimization

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NEURAL COMPUTING & APPLICATIONS 2018年第10期30卷 3117-3144页

作者： de Melo, Vinicius Veloso Banzhaf, Wolfgang Univ Fed Sao Paulo Inst Sci & Technol Sao Jose Dos Campos SP Brazil Michigan State Univ BEACON Ctr Study Evolut Act Dept Comp Sci & Engn E Lansing MI 48864 USA

This paper proposes Drone Squadron optimization (DSO), a new self-adaptive metaheuristic for global numerical optimization which is updated online by a hyper-heuristic. DSO is an artifact-inspired technique, as opposed to many nature-inspired algorithms used today. DSO is very flexible because it is not related to natural behaviors or phenomena. DSO has two core parts: the semiautonomous drones that fly over a landscape to explore, and the command center that processes the retrieved data and updates the drones' firmware whenever necessary. The self-adaptive aspect of DSO in this work is the perturbation/movement scheme, which is the procedure used to generate target coordinates. This procedure is evolved by the command center during the global optimization process in order to adapt DSO to the search landscape. We evaluated DSO on a set of widely employed single-objective benchmark functions. The statistical analysis of the results shows that the proposed method is competitive with the other methods, but we plan several future improvements to make it more powerful and robust.

关键词： global numerical optimization Hyper-heuristic Metaheuristic Self-adaptive Genetic programming Coevolution

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A novel evolutionary algorithm for global numerical optimization with continuous variables

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Progress in Natural Science:Materials International 2008年第3期18卷 345-351页

作者： Wenhong Zhaoa,b, Wei Wang a, Yuping Wang a, a School of Computer Science and Technology, Xidian University, Xi’an 710071, China b Faculty of Science, Xidian University, Xi’an 710071, China School of Computer Science and Technology Xidian University Xi’an 710071 China Faculty of Science Xidian University Xi’an 710071 China

Evolutionary algorithms (EAs) are a class of general optimization algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. In this paper, a novel evolutionary algorithm is proposed to solve global numerical optimiza- tion with continuous variables. In order to make the algorithm more robust, the initial population is generated by combining determinate factors with random ones, and a decent scale function is designed to tailor the crossover operator so that it can not only find the decent direction quickly but also keep scanning evenly in the whole feasible space. In addition, to improve the performance of the algorithm, a mutation operator which increases the convergence-rate and ensures the convergence of the proposed algorithm is designed. Then, the global convergence of the presented algorithm is proved in detail. Finally, the presented algorithm is executed to solve 24 benchmark problems, and the results show that the convergence-rate of the proposed algorithm is much faster than that of the compared algorithms.

关键词： Evolutionary algorithm Decent scale function global numerical optimization global convergence

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Random neighbor elite guided differential evolution for global numerical optimization

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INFORMATION SCIENCES 2022年 607卷 1408-1438页

作者： Yang, Qiang Yan, Jia-Qi Gao, Xu-Dong Xu, Dong-Dong Lu, Zhen-Yu Zhang, Jun Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Sch Future Technol Nanjing 210044 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310000 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea

optimization problems not only become more and more ubiquitous in various fields, but also become more and more difficult to optimize nowadays, which seriously challenge the effectiveness of existing optimizers like different evolution (DE). To effectively solve this kind of problems, this paper proposes a random neighbor elite guided differential evo-lution (RNEGDE) algorithm. Specifically, to let individuals explore and exploit the solution space properly, a novel random neighbor elite guided mutation strategy named "DE/cur rent-to-rnbest/1" is first proposed to mutate individuals. In this mutation strategy, several individuals randomly selected from the population for each individual to be updated along with the individual itself form a neighbor region, and then the best one in such a region is adopted as the guiding exemplar to mutate the individual. Due to the random selection of neighbors and the directional guidance of elites, this strategy is expected to direct individ-uals to promising areas fast without serious loss of diversity. Notably, it is found that two popular mutation strategies, namely "DE/current-to-best/1" and "DE/current-to-pbest/1", are two special cases of the proposed "DE/current-to-rnbest/1". Further, to alleviate the sensitivity of the proposed algorithm to the involved parameters, this paper utilizes the Gaussian distribution and the Cauchy distribution to adaptively generate parameter values for each individual with the mean value of the Gaussian distribution and the position value of the Cauchy distribution adaptively adjusted based on the evolutionary information of the population. With the above two techniques, the proposed algorithm is expected to effectively search the solution space. At last, extensive experiments conducted on one widely used benchmark function set with three different dimension sizes demonstrate that the proposed algorithm achieves highly competitive or even much better performance than several compared state-of-t

关键词： Differential evolution Random neighbor elite guided mutation Adaptive parameter adjustment global numerical optimization Evolutionary algorithms

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Dynamic partition search algorithm for global numerical optimization

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APPLIED INTELLIGENCE 2014年第4期41卷 1108-1126页

作者： Sun, Gaoji Zhao, Ruiqing Tianjin Univ Inst Syst Engn Tianjin 300072 Peoples R China

This paper presents a novel evolutionary algorithm entitled Dynamic Partition Search Algorithm (DPSA) for global optimization problems with continuous variables. The DPSA is a population-based stochastic search algorithm in nature, which mainly consists of initialization process and evolution process. In the initialization process, the DPSA randomly generates an initial population of members from a specific search space and finds a leader. In the evolution process, the DPSA applies two groups to balance exploration ability and exploitation ability, in which one group is in charge of exploring new region via a dynamic partition strategy, and the other group relies on Cauchy distributions to exploit the region around the best member. Later, numerical experiments are conducted for 24 classical benchmark functions with 100, 1000 or even 10000 dimensions. And the performance of the proposed DPSA is compared with a state-of-the-art cooperative coevolving particle swarm optimization (CCPSO2), and two existing differential evolution (DE) algorithms. The experimental results show that DPSA has a better performance than the algorithms used for comparison, especially for high dimensional optimization problems. In addition, the numerical computational results also demonstrate that the DPSA has good scalability, and it is an effective evolutionary algorithm for solving large-scale global optimization problems.

关键词： global numerical optimization Evolutionary algorithm Dynamic partition search algorithm

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Differential evolution with guiding archive for global numerical optimization

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APPLIED SOFT COMPUTING 2016年 43卷 424-440页

作者： Zhou, Yalan Wang, Jiahai Zhou, Yuren Qiu, Zhanyan Bi, Zhisheng Cai, Yiqiao Guangdong Univ Finance & Econ Coll Informat Guangzhou 510320 Guangdong Peoples R China Sun Yat Sen Univ Dept Comp Sci Guangzhou 510006 Guangdong Peoples R China Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou 510006 Guangdong Peoples R China Guangzhou Med Univ Sch Basic Sci Guangzhou 510182 Guangdong Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

Differential evolution (DE) is a simple, yet efficient, population-based global evolutionary algorithm. DE may suffer from stagnation. This study presents a DE framework with guiding archive (GAR-DE) to help DE escape from the situation of stagnation. The proposed framework constructs a guiding archive and executes stagnation detection at each iteration. Guiding archive is composed of a certain number of relatively high-quality solutions. These solutions are collected in terms of fitness as well as diversity. If a stagnated individual is detected, the proposed framework selects a solution from guiding archive to replace the base vector in mutation operator. In this way, more promising solutions are provided to guide the evolution and effectively help DE escape from the situation of stagnation. The proposed framework is applied to six original DE algorithms, as well as two advanced DE variants. Experimental results on 28 benchmark functions and 8 real-world application problems show that the proposed framework can enhance the performance of most DE algorithms studied. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Differential evolution Stagnation Guiding archive global numerical optimization

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Chaotic bee colony algorithms for global numerical optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第8期37卷 5682-5687页

作者： Alatas, Bilal Firat Univ Fac Engn Dept Comp Engn TR-23119 Elazig Turkey

Artificial bee colony (ABC) is the one of the newest nature inspired heuristics for optimization problem. Like the chaos in real bee colony behavior, this paper proposes new ABC algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions. This has been done by using of chaotic number generators each time a random number is needed by the classical ABC algorithm. Seven new chaotic ABC algorithms have been proposed and different chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of ABC and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： Bee colony algorithm global numerical optimization Chaos

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Simulated annealing based artificial bee colony algorithm for global numerical optimization

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APPLIED MATHEMATICS AND COMPUTATION 2012年第8期219卷 3575-3589页

作者： Chen, Shi-Ming Sarosh, Ali Dong, Yun-Feng Beijing Univ Aeronaut & Astronaut Sch Astronaut Beijing 100191 Peoples R China

Artificial bee colony (ABC) algorithm is a global optimization algorithm, which has been shown to be competitive with some conventional swarm algorithm, such as genetic algorithm (GA) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm, in that it has poor convergence rate in some situations. Inspired by simulated annealing algorithm, a simulated annealing based ABC algorithm (SAABC) is proposed. Simulated annealing algorithm is introduced into employed bees search process to improve the exploitation of the algorithm. The experimental results are tested on a set of numerical benchmark functions with different dimensions. That show that SAABC algorithm can outperform ABC and global best guided ABC algorithms in most of the experiments. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Artificial bee colony Simulated annealing algorithm global best guided ABC global numerical optimization Swarm intelligence optimization

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A perturb biogeography based optimization with mutation for global numerical optimization

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APPLIED MATHEMATICS AND COMPUTATION 2011年第2期218卷 598-609页

作者： Li, Xiangtao Wang, Jinyan Zhou, Junping Yin, Minghao NE Normal Univ Coll Comp Sci Changchun 130117 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

Biogeography based optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. We propose three extensions to BBO. First, we propose a new migration operation based sinusoidal migration model called perturb migration, which is a generalization of the standard BBO migration operator. Then, the Gaussian mutation operator is integrated into perturb biogeography based optimization (PBBO) to enhance its exploration ability and to improve the diversity of population. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. Simulation results and comparisons demonstrate the proposed PBBO algorithm using sinusoidal migration model is better, or at least comparable to, the RCBBO based linear model, RCBBO-G, RCBBO-L and evolutionary algorithms from literature when considering the quality of the solutions obtained. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Biogeography based optimization Perturb opertor global numerical optimization Exploration Exploitation

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