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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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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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Optimizing thermal load and power scaling in Er³⁺-doped ZBLAN fiber lasers

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OPTICAL FIBER TECHNOLOGY 2021年 64卷 102507-102507页

作者： Gursel, Amira Tandirovic Kilic, Fatih Adana Alparslan Turkes Sci & Technol Univ Elect & Elect Engn Dept Adana Turkey Adana Alparslan Turkes Sci & Technol Univ Comp Engn Dept Adana Turkey

Thanks to characteristics such as high beam qualities and high average powers, largely founded on the outstanding characteristics of fibers as an active medium, fiber lasers have become one of the most popular laser technologies. Although most of the advantages of fiber lasers stem from their geometry, the same geometry is the main source of the limitations. In this study, we focused on managing the thermal load by variable absorption coefficient in order to increase the thermal limits, which are an important limitation of further development in fiber technology. Metaheuristic optimization approaches can be used to improve the performance of fiber technologies with physical constraints. The main aim of metaheuristic optimization is to improve the output of the function with respect to the parameters considered as significant. Swarm optimization approach is one of the powerful metaheuristic approaches proven to be quite promising to address the above issues. In this study, the optimization of the thermal management of the Er3+-doped ZBLAN fiber laser is performed using Artificial Bee Colony Algorithm (ABC). Additionally, this paper proposes four new strategies of initial parameter setting, repair parameter values, and fitness function for the thermal load of the Er3+-doped ZBLAN fiber laser with respect to the output power. The results show that the absorption coefficient functions obtained by ABC algorithm give more effective and efficient results than the obtained rising functions.

关键词： Fiber lasers Thermal management Power scaling Artificial bee colony algorithm 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 PSO based approach: Scout particle swarm algorithm for continuous global optimization problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2019年第2期6卷 129-142页

作者： Koyuncu, Hasan Ceylan, Rahime Konya Tech Univ Elect & Elect Engn Dept Konya Turkey

In the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm optimization (PSO) and an efficient operator of Artificial Bee Colony optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (P-best). Thus, the need for external intervention is inevitable once a useful particle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants;the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of Bit

关键词： Scout particle swarm optimization numerical function optimization Particle swarm optimization Artificial bee colony optimization Hybrid approach

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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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Firefly photinus search algorithm

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2020年第5期32卷 599-607页

作者： Alomoush, Waleed Omar, Khairuddin Alrosan, Ayat Alomari, Yazan M. Albashish, Dheeb Almomani, Ammar Imam Abdulrahman Bin Faisal Univ Dept CS Dammam Saudi Arabia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Malaysia Univ Sains Islam Malaysia Fac Sci & Technol Comp Sci Dept Nilai Malaysia Al Balqa Appl Univ Prince Abdullah Bin Ghazi Fac Informat Technol Comp Sci Dept Salt Jordan Al Balqa Appl Univ Al Huson Univ Coll IT Dept POB 50 Irbid Jordan

Firefly Algorithm (FA) is one of the new natural inspired optimization algorithms. It is inspired by the flashing behavior of the fireflies. Firefly algorithm, has some drawbacks such as getting trapped into several local optima, FA parameters are set fixed without change during iterations time. Besides that, it does not memorize or remember the history of any situation for each iteration. In this paper, we propos a firefly photinus algorithm (FPA) based on the initialize mate list to solve problems of trapped into several local optima and remember history of situation to forbidden fireflies movements in mate list (history) during the search process, and propose new absorption parameter r to change the parameters during iterations time which lead to balance between exploration and exploitation, and it controls the dominance area of a lighter firefly during time iterations by reduction or increase r coefficient whether. The experimental results tested on thirteen benchmark functions are selected to evaluate performance of the FPA and to compare it with the standards of the FA and Some FA variants algorithm, it show that FPA algorithm can outperform FA and FA variants algorithm in most of the experiments. (C) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

关键词： Firefly algorithm Natural inspired optimization algorithm numerical function optimization

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Particle state change algorithm

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SOFT COMPUTING 2018年第8期22卷 2641-2666页

作者： Feng, Xiang Xu, Hanyu Yu, Huiqun Luo, Fei East Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Smart City Collaborat Innovat Ctr Shanghai Peoples R China

The matter state change, which is a common phenomenon in nature, shows the process that the matter how to reach the optimal state in the environment. This paper presents a novel particle state change model (PSCM) inspired by mimicking the process of the matter state change. Based on PSCM, a novel particle state change (PSC) algorithm is proposed for solving continuous optimization problems. As a new algorithm, PSC has many differences from other similar nature-inspired algorithms in terms of the basic principle models, mathematical formalization and properties. This paper considers three states of the matter, namely gas state, liquid state and solid state. In a certain circumstance, the matter always converts from an unstable state into a stable state. It is similar to find the optimal solution of an optimization problem. The proposed algorithm also has the advantages in the respects of higher intelligence, effectiveness and lower computation complexity. And the convergence property of PSC is discussed in detail. In order to illustrate the ability of solving optimization problems in continuous domain, the new proposed algorithm is tested on basic function optimization, CEC2016 single-objective real-parameter numerical optimization and CEC2016 learning-base real-parameter single-objective optimization, and compared with eleven existing algorithms. The numerous simulations have shown the effectiveness and suitability of the proposed approach.

关键词： Particle state change algorithm Nature-inspired algorithm Matter state change numerical function optimization

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A Multilevel Cooperative Multi-Population Cultural Algorithm

A Multilevel Cooperative Multi-Population Cultural Algorithm

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IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications (INISTA)

作者： Singh, Dilpreet Zadeh, Pooya Moradian Kobti, Ziad Univ Windsor Sch Comp Sci Windsor ON Canada

ISBN: (纸本)9781538651506

A new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new Multilevel Selection framework (ML-MPCA). The approach used in this paper is based on biological group selection theory which aims to improve the capability of MPCA to tackle evolution of cooperation. A two-level selection process is introduced namely within-group selection and between-group selection. Individuals interact with the other members of the group in an evolutionary game that determines their fitness. If the group reaches a certain size, it splits into two daughter groups. We test our algorithm on CEC 2015 expensive benchmark functions to evaluate its performance. We show that our proposed algorithm improves solution accuracy and consistency. The model can be extended to more than two levels of selection and can also include migration.

关键词： Multilevel/Group Selection Evolutionary Computation Evolution of Cooperation numerical function optimization

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