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A Novel Adaptive Cooperative Artificial Bee Colony Algorithm for Solving numerical function optimization 16th

A Novel Adaptive Cooperative Artificial Bee Colony Algorithm...

Joint Conference of the 16th Asia Simulation Conference / SCS International Autumn Simulation Multi-Conference (AsiaSim/SCS AutumnSim)

作者： Liu, Bin Li, Wei-min Pan, Shuai AFEU Air & Missile Def Coll Xian Peoples R China

ISBN: (纸本)9789811026638;9789811026621

Considering the disadvantages of the traditional Artificial Bee Colony (ABC) Algorithm, a Novel Adaptive Cooperative Artificial Bee Colony (ACABC) Algorithm is proposed in this paper. Some ideas including dividing bee swarm into two parts: main-bee swarm and vice-bee swarm, bringing a judge principle for local optimum points and narrowing bee swarm search interval adaptively are introduced and consequently served as the core of novel Adaptive Cooperative Artificial Bee Colony Algorithm. The performance of PSO, DE, ABC and ACABC algorithms are compared using 10 kinds benchmark testing functions. Simulation results show ACABC owns better optimizing precision and speed and preferable anti-jamming ability, which can be applied for solving other complicated optimization problems.

关键词： Artificial Bee Colony Algorithm Adaptively narrow search interval Swarm cooperation Escaping from local optimum points numerical function optimization

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A Modified League Championship Algorithm for numerical function optimization Via Artificial Modeling of the "Between Two Halves Analysis"

A Modified League Championship Algorithm for Numerical Funct...

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6th International Conference on Soft Computing and Intelligent Systems (SCIS) / 13th International Symposium on Advanced Intelligence Systems (ISIS)

作者： Kashan, Ali Husseinzadeh Karimiyan, Somayyeh Karimiyan, Morteza Kashan, Mina H. Islamic Azad Univ Dept Syst & Ind Engn Sci & Res Branch Tehran Iran Int Inst Earthquake Engn & Seismol Dept Earthquake Engn Tehran Iran Islamic Azad Univ Dept Earthquake Engn Sci & Res Branch Tehran Iran Tarbiat Modares Univ Fac Engn Dept Ind Engn Tehran Iran

ISBN: (纸本)9781467327435;9781467327428

Inspired by the competition of sport teams in a sport league, the League Championship Algorithm (LCA) has been introduced recently for optimizing nonlinear continuous functions. LCA tries to metaphorically model a league championship environment wherein a number of individuals, as artificial sport teams, play in pairs in an artificial league for several weeks (iterations) based on a league schedule. Given the playing strength (fitness value) along with a team intended formation (solution) in each week, the game outcome is determined in terms of win or loss and this will serve as a basis to direct the search toward fruitful areas. At the heart of LCA is the artificial post-match analysis where, to generate a new solution, the algorithm imitates form the strengths/weaknesses/opportunities/threats (SWOT) based analysis followed typically by coaches to develop a new team formation for their next week contest. In this paper we try to modify the basic algorithm via modeling a between two halves like analysis beside the postmatch SWOT analysis to generate new solutions. Performance of the modified algorithm is tested with that of basic version and the particle swarm optimization algorithm (PSO) on finding the global minimum of a number of benchmark functions. Results testify that the improved algorithm called RLCA, performs well in terms of both final solution quality and convergence speed.

关键词： numerical function optimization league championship algorithm particle swarm optimization algorithm

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Artificial Bee Group Colony Algorithm for numerical function optimization

Artificial Bee Group Colony Algorithm for Numerical Function...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Yang, Gang Xu, Jieping Yi, Junyan Zheng, He Yuan, Zheng Liu, Xiaowei Renmin Univ China Informat Sch Mutlimedia Comp Lab Beijing Peoples R China Beijing Univ Civil Engn & Architecture Beijing Peoples R China Beijing Applicat Inst Informat Technol Beijing Peoples R China

ISBN: (纸本)9781479986965

In this paper, we propose an artificial bee group colony algorithm for numerical function optimization, based on the thoughts of group competition and similar property characteristic. Our algorithm contains three optimization strategies including grouping strategy, similar property strategy and competition strategy, which could not only ensure the algorithm finds better solutions stably, but also induce the algorithm to maintain solution diversification. Moreover, the similar property strategy could produce efficient exploring to find better solutions with skipping optimization. We evaluated the performance of our proposed algorithm on some standard numerical benchmark functions. The results demonstrate that our algorithm is able to yield higher quality solutions with faster convergence than either the original ABC or some other authoritative swarm intelligent algorithms.

关键词： Artificial bee colony grouping strategy group competition similar property strategy numerical function optimization

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Hybrid Artificial Bee Colony Algorithm with Differential Evolution and Free Search for numerical function optimization

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2016年第4期25卷 1650020-1650020页

作者： Lian Lian Fu Zaifeng Yang Guangfei Huang Yi Dalian Univ Technol Sch Transportat & Logist Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Fac Econ & Management Dalian 116024 Liaoning Peoples R China

Artificial bee colony (ABC) algorithm invented by Karaboga has been proved to be an efficient technique compared with other biological-inspired algorithms for solving numerical optimization problems. Unfortunately, convergence speed of ABC is slow when working with certain optimization problems and some complex multimodal problems. Aiming at the shortcomings, a hybrid artificial bee colony algorithm is proposed in this paper. In the hybrid ABC, an improved search operator learned from Differential Evolution (DE) is applied to enhance search process, and a not-so-good solutions selection strategy inspired by free search algorithm (FS) is introduced to avoid local optimum. Especially, a reverse selection strategy is also employed to do improvement in onlooker bee phase. In addition, chaotic systems based on the tent map are executed in population initialization and scout bee's phase. The proposed algorithm is conducted on a set of 40 optimization test functions with different mathematical characteristics. The numerical results of the data analysis, statistical analysis, robustness analysis and the comparisons with other state-of-the-art-algorithms demonstrate that the proposed hybrid ABC algorithm provides excellent convergence and global search ability.

关键词： Artificial bee colony algorithm differential evolution free search selection chaotic systems numerical function optimization

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A hybrid artificial bee colony algorithm for numerical function optimization

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2015年第10期26卷 1550109-1550109页

作者： Alqattan, Zakaria N. Abdullah, Rosni Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms;it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

关键词： Swarm intelligence algorithms artificial bee colony algorithm particle swarm optimization numerical function optimization

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A Modified League Championship Algorithm for numerical function optimization Via Artificial Modeling of the "Between Two Halves Analysis"

A Modified League Championship Algorithm for Numerical Funct...

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International Conference on Soft Computing and Intelligent Systems

作者： Ali Husseinzadeh Kashan Somayyeh Karimiyan Morteza Karimiyan Mina H. Kashan Department of Systems and Industrial Engineering Science and Research Branch Islamic Azad University Department of Earthquake Engineering International Institute of Earthquake Engineering and Seismology Department of Earthquake Engineering Science and Research Branch Islamic Azad University Department of Industrial Engineering Faculty of Engineering Tarbiat Modares University

ISBN: (纸本)9781467327428

Inspired by the competition of sport teams in a sport league, the League Championship Algorithm (LCA) has been introduced recently for optimizing nonlinear continuous functions. LCA tries to metaphorically model a league championship environment wherein a number of individuals, as artificial sport teams, play in pairs in an artificial league for several weeks (iterations) based on a league schedule. Given the playing strength (fitness value) along with a team intended formation (solution) in each week, the game outcome is determined in terms of win or loss and this will serve as a basis to direct the search toward fruitful areas. At the heart of LCA is the artificial post-match analysis where, to generate a new solution, the algorithm imitates form the strengths/weaknesses/opportunities/threats (SWOT) based analysis followed typically by coaches to develop a new team formation for their next week contest. In this paper we try to modify the basic algorithm via modeling a between two halves like analysis beside the post-match SWOT analysis to generate new solutions. Performance of the modified algorithm is tested with that of basic version and the particle swarm optimization algorithm (PSO) on finding the global minimum of a number of benchmark functions. Results testify that the improved algorithm called RLCA, performs well in terms of both final solution quality and convergence speed.

关键词： numerical function optimization League championship algorithm Particle swarm optimization algorithm

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Disruption: A new operator in gravitational search algorithm

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SCIENTIA IRANICA 2011年第3期18卷 539-548页

作者： Sarafrazi, S. Nezamabadi-pour, H. Saryazdi, S. Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

To improve the exploration and exploitation abilities of the standard Gravitational Search Algorithm (GSA), a novel operator called "Disruption", originating from astrophysics, is proposed. The disruption operator is inspired by nature and, with the least computation, has improved the ability of GSA to further explore and exploit the search space. The proposed improved GSA has been evaluated on 23 nonlinear benchmark functions and compared with standard GSA, the genetic algorithm and particle swarm optimization. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. (C) 2011 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.

关键词： numerical function optimization Heuristic search algorithm Swarm intelligence Gravitational search algorithm Disruption operator

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A novel optimization algorithm inspired by the creative thinking process

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SOFT COMPUTING 2015年第10期19卷 2955-2972页

作者： Feng, Xiang Zou, Ru Yu, Huiqun Chinese Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China

Creative thinking, which plays an essential role in the progress of human society, has an outstanding problem-solving ability. This paper presents a novel creativity-oriented optimization model(COOM) and algorithm (COOA) inspired by the creative thinking process. At first, COOM is constructed by simplifying the procedure of creative thinking-while retaining its main characteristics. And then, COOA is presented for continuous optimization problems. It is a realization of COOM. As a new nature-inspired algorithm, COOA is different from other similar algorithms in terms of the basic principle, mathematical formalization and properties. Features of the COOM and the corresponding algorithm include a powerful processing ability for the complex problems, namely high-dimensional, highly nonlinear and random problems. The proposed approach also has the advantages in terms of the higher intelligence, effectiveness, parallelism and lower computation complexity. The properties of COOA, including convergence and parallelism, are discussed in detail. The numerous simulations on the CEC-2013 real-parameter optimization benchmark functions' problems have shown the effectiveness and parallelism of the proposed approach.

关键词： Creativity-oriented optimization algorithm Nature-inspired algorithm Creative thinking numerical function optimization

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A chaotic teaching learning based optimization algorithm for clustering problems

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APPLIED INTELLIGENCE 2019年第3期49卷 1036-1062页

作者： Kumar, Yugal Singh, Pradeep Kumar JUIT Dept Comp Sci & Engn Waknaghat Himachal Pardes India

This paper presents a teaching learning based algorithm for solving optimization problems. This algorithm is inspired through classroom teaching pattern either students can learn from teachers or from other students. But, the teaching learning based optimization (TLBO) algorithm suffers with premature convergence and lack of tradeoff between local search and global search. Hence, to address the above mentioned shortcomings of TLBO algorithm, a chaotic version of TLBO algorithm is proposed with different chaotic mechanisms. Further, a local search method is also incorporated for effective tradeoff between local and global search and also to improve the quality of solution. The performance of proposed algorithm is evaluated on some benchmark test functions taken from Congress on Evolutionary Computation 2014 (CEC'14). The results revealed that proposed algorithm provides better and effective results to solve benchmark test functions. Moreover, the proposed algorithm is also applied to solve clustering problems. It is found that proposed algorithm gives better clustering results in comparison to other algorithms.

关键词： Teaching learning-based optimization Clustering Meta-heuristics numerical function optimization

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A Bird Flock Gravitational Search Algorithm Based on the Collective Response of Birds

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COMPUTER JOURNAL 2017年第11期60卷 1687-1716页

作者： Han, XiaoHong Qiang, Yan Lan, Yuan Taiyuan Univ Technol Minist Educ China Key Lab Adv Transducers & Intelligent Control Sys Taiyuan Shanxi Peoples R China

Gravitational search algorithm (GSA) is a stochastic search algorithm based on the law of gravity and mass which is widely used nowadays for efficient solution of optimization problems. For the purpose of enhancing the performance of original GSA, this paper proposes a new GSA called Bird Flock Gravitational Search Algorithm (BFGSA) based on the collective response of birds. Although GSA performs well in many problems, algorithms in this category lack mechanisms which add diversity to exploration in the search process. Our proposed algorithm introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main major steps including initialization, identification of the nearest neighbors and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly composed benchmark functions presented in CEC2005 competition and the results are compared with recent variants of the original particle swarm optimization and state-of-the-art GSA algorithms. Furthermore, we applied BFGSA to a real-world application of data clustering. The results show that BFGSA improves the performance of the original GSA and obtains the best results compared with our selected GSA-type algorithms in benchmarking experiments and clustering experiments.

关键词： gravitational search algorithm learning algorithm heuristic search algorithm collective behavior numerical function optimization data clustering

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