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 exploit...
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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 numericaloptimization 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 numericaloptimization problems. The results demonstrate that the modified ABC algorithm outperforms the ABC algorithm on solution quality and faster convergence.
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 algor...
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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.
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 lea...
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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.
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 t...
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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.
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. ...
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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.
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 optimizati...
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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 functionoptimization. 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
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 lea...
详细信息
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.
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 l...
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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.
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) insp...
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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 functionoptimization, CEC2016 single-objective real-parameter numericaloptimization 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.
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 w...
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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.
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