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A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm

JOURNAL OF GLOBAL optimization 2007年第3期39卷 459-471页

作者： Karaboga, Dervis Basturk, Bahriye Erciyes Univ Dept Comp Engn Kayseri Turkey

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.

关键词： swarm intelligence artificial bee colony particle swarm optimization genetic algorithm particle swarm inspired evolutionary algorithm numerical function optimization

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Root growth model: a novel approach to numerical function optimization and simulation of plant root system

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SOFT COMPUTING 2014年第3期18卷 521-537页

作者： Zhang, Hao Zhu, Yunlong Chen, Hanning Chinese Acad Sci Shenyang Inst Automat Key Lab Ind Informat Shenyang 110016 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China

This paper presents a general optimization model gleaned ideas from root growth behaviours in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for optimization of higher-dimensional numerical function. For this study, a mathematical framework and architecture are designed to model root growth patterns of plant. Under this architecture, the interactions between the soil and root growth are investigated. A novel approach called "root growth algorithm" (RGA) is derived in the framework and simulation studies are undertaken to evaluate this algorithm. The simulation results show that the proposed model can reflect the root growth behaviours of plant in the soil and the numerical results also demonstrate RGA is a powerful search and optimization technique for higher-dimensional numerical function optimization.

关键词： Root growth Simulation numerical function optimization Higher-dimensional

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A novel bee swarm optimization algorithm for numerical function optimization

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COMMUNICATIONS IN NONLINEAR SCIENCE AND numerical SIMULATION 2010年第10期15卷 3142-3155页

作者： Akbari, Reza Mohammadi, Alireza Ziarati, Koorush Shiraz Univ Dept Comp Sci & Engn Shiraz Iran

The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSORP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust;produce excellent results, and outperform other algorithms investigated in this consideration. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Bee swarm optimization numerical function optimization Time-varying weights Repulsion factor

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A genetic algorithm with multi-parent crossover using quaternion representation for numerical function optimization

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APPLIED INTELLIGENCE 2017年第4期46卷 810-826页

作者： Thanh Tung Khuat My Hanh Le Univ Danang Univ Sci & Technol Danang Vietnam

Finding optimal solutions of a numerical function of more than one independent variable is an important problem with many practical applications including process control systems, data fitting, and engineering designs. Over the last few decades, techniques for solving unconstrained optimization problems have been proposed. Evolutionary Algorithms have emerged as one of the most popular selections for tackling these problems, among which Genetic Algorithms (GAs) are widely used in practice. In recent literature on GAs, a Genetic Algorithm with multi-parent crossover (GA-MPC) was found to be superior over other algorithms. Nevertheless, the GA-MPC still has some difficulties when dealing with separable test issues and convergence to global optima in the high-dimensional search space. Meanwhile, quaternions, which are an extension of complex numbers, can allow algorithms to expand the search space to avoid getting stuck in the local optima. Therefore, this study aims to employ quaternions for representing individuals in the GA-MPC to enhance the effectiveness of the GA-MPC. Experimental results for ten benchmark functions indicated that the GA-MPC using the quaternion representation of individuals improved the quality of solutions compared with the original GA-MPC.

关键词： numerical function optimization Genetic algorithms Multi-parent crossover Quaternions Representation of individuals

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Artificial bee colony algorithm with gene recombination for numerical function optimization

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APPLIED SOFT COMPUTING 2017年 52卷 146-159页

作者： Li, Genghui Cui, Laizhong Fu, Xianghua Wen, Zhenkun Lu, Nan Lu, Jian Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen Peoples R China

Artificial bee colony (ABC) algorithm is a stochastic and population-based optimization method, whichmimics the collaborative foraging behaviour of honey bees and has shown great potential to handle various kinds of optimization problems. However, ABC often suffers from slow convergence speed since its internal mechanism and solution search equation do well in exploration, but badly in exploitation. In order to solve this knotty issue, inspired by the natural phenomenon that the good individuals (solutions) always contain good genes (variables) and the effective combination of the superior genes from different good individuals could more easily produce better offspring, we introduce a novel gene recombination operator (GRO) into ABC to accelerate convergence. To be specific, in GRO, a part of good solutions in the current population are selected to produce candidate solutions by the gene combination. Especially, each good solution recombines with only one other good solution to generate only one candidate solution. In addition, GRO will be launched at the end of each generation. In order to validate its efficiency and effectiveness, GRO is embedded into nine versions of ABC, i.e., the original ABC, GABC, best-so-farABC( BSFABC), MABC, CABC, ABCVSS, qABC, dABC and distABC, while yields GRABC, GRGABC, GRBSFABC, GRMABC, GRCABC, GRABCVSS, GRqABC, GRdABC and GRdistABC respectively. The experimental results on 22 benchmark functions demonstrate that GRO could enhance the exploitation ability of ABCs and accelerate convergence without loss of diversity. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Swarm intelligence Artificial bee colony Gene recombination numerical function optimization

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Gbest-guided artificial bee colony algorithm for numerical function optimization

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APPLIED MATHEMATICS AND COMPUTATION 2010年第7期217卷 3166-3173页

作者： Zhu, Guopu Kwong, Sam City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Artificial bee colony (ABC) algorithm invented recently by Karaboga is a biological-inspired optimization algorithm, which has been shown to be competitive with some conventional biological-inspired algorithms, such as genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by PSO, we propose an improved ABC algorithm called gbest-guided ABC (GABC) algorithm by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation. The experimental results tested on a set of numerical benchmark functions show that GABC algorithm can outperform ABC algorithm in most of the experiments. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Artificial bee colony algorithm Genetic algorithm Particle swarm optimization Differential evolution Biological-inspired optimization algorithm numerical function optimization

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A quantum inspired gravitational search algorithm for numerical function optimization

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INFORMATION SCIENCES 2014年 267卷 83-100页

作者： Soleimanpour-moghadam, Mohadeseh Nezamabadi-pour, Hossein Farsangi, Malihe M. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

Gravitational search algorithm (GSA) is a swarm intelligence optimization algorithm that shares many similarities with evolutionary computation techniques. However, the GSA is driven by the simulation of a collection of masses which interact with each other based on the Newtonian gravity and laws of motion. Inspired by the classical GSA and quantum mechanics theories, this work presents a novel GSA using quantum mechanics theories to generate a quantum-inspired gravitational search algorithm (QIGSA). The application of quantum mechanics theories in the proposed QIGSA provides a powerful strategy to diversify the algorithm's population and improve its performance in preventing premature convergence to local optima. The simulation results and comparison with nine state-of-the-art algorithms confirm the effectiveness of the QIGSA in solving various benchmark optimization functions.(C) 2013 Elsevier Inc. All rights reserved.

关键词： Swarm intelligence Gravitational search algorithm Quantum computing numerical function optimization

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Quantum-inspired algorithm with fitness landscape approximation in reduced dimensional spaces for numerical function optimization

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INFORMATION SCIENCES 2020年 527卷 253-278页

作者： Mu, Lei Wang, Peng Xin, Gang Southwest Minzu Univ Sch Comp Sci & Technol Chengdu 610041 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu 610041 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In realistic numerical optimization problems, the efficiency of the algorithm is a crucial consideration. Fitness landscape approximation is beneficial for improving the search efficiency in evolutionary algorithms, but this method increases the computational cost significantly with an increase in the dimension. This paper proposes a quantum-inspired algorithm with dimension decomposition and fitness landscape approximation. The high-dimensional problem space is transformed into a low-dimensional projection space for fitness landscape approximation through mapping transformation. Linear approximation for the fitness landscape is performed in the transformed multiple 1D projected spaces. In accordance with two different diversity-retaining strategies, reference sampling points with different characteristics are selected to perform two-point linear approximation. Candidate points in the projection space are estimated and then synthesized as candidate solutions in the problem space. Then, candidate solutions are used in an appropriate population reconstruction mechanism to improve the performance of the algorithm. The proposed algorithms are implemented on benchmark functions abstracted from real-world optimization problems and compared with several algorithms. Experimental results on errors, success rates, and their rankings show the superiority of the proposed approach with the solution diversity priority with regard to effectiveness, adaptability, and stability. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Landscape approximation Dimensionality reduction numerical function optimization Quantum behavior Population reconstruction

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A modified artificial bee colony algorithm for numerical function optimization

A modified artificial bee colony algorithm for numerical fun...

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17th IEEE Symposium on Computers and Communications (lSCC)

作者： Babayigit, Bilal Ozdemir, Resul Erciyes Univ Dept Comp Engn Kayseri Turkey Nevsehir Univ Urgup USET Vocat Sch Nevsehir Turkey

ISBN: (纸本)9781467327138

Artificial bee colony (ABC) algorithm, explored in recent literature, is an efficient optimization technique which simulates the foraging behavior of honeybees. ABC algorithm is good at exploration but poor at exploitation. This paper presents a new modified ABC algorithm for numerical optimization problems to improve the exploitation capability of the ABC algorithm. A different probability function and a new searching mechanism are proposed. The modified ABC algorithm is tested on seven numerical optimization problems. The results demonstrate that the modified ABC algorithm outperforms the ABC algorithm on solution quality and faster convergence.

关键词： Swarm intelligence modified artificial bee colony algorithm numerical function optimization

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Cellular Differentiation Algorithm for High Dimensional numerical function optimization

Cellular Differentiation Algorithm for High Dimensional Nume...

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11th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Wang, Yanjiang Yuan, Chengna Liu, Weifeng China Univ Petr East China Coll Informat & Control Engn Qingdao Peoples R China

ISBN: (纸本)9780769549132;9781467346511

Inspired by the cellular differentiation mechanism of organisms, combined with the theory of artificial life and swarm intelligence, a new biomimetic optimization algorithm, cellular differentiation optimization algorithm (CDOA), is proposed in this paper. A certain number of cells are randomly distributed in the search space to find the optimal solution by activating their differential behaviors such as division, growth, migration, adhesion and apoptosis. Experimental results on several benchmark complex functions with high dimensions show that the proposed cellular differentiation optimization algorithm can rapidly converge at high quality solutions and outperform some of the state-of-art in high-dimension numerical function optimization.

关键词： cellular differentiation optimization algorithm biomimetic swarm intelligence complex function with high imensions numerical function optimization

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