Magnetic optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two asp...
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Magnetic optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algorithm's past experience, an adaptive parameter control strategy which dynamically sets the parameters of the algorithm during the optimization is proposed. To show the significance of the proposed parameter adaptation strategy, we compare the algorithm with two well-known parameter setting techniques on a number of benchmark problems. The results indicate that although the proposed algorithm with the adaptation strategy does not require to set the parameters of the algorithm prior to the optimization process, it outperforms MOA with other parameter setting strategies in most large-scale optimizationproblems. We also study the algorithm while employing the OBL by comparing it with the original version of MOA. Furthermore, the proposed algorithm is tested and compared with seven traditional population-based algorithms and eight state-of-the-art optimization algorithms. The comparisons demonstrate that the proposed algorithm outperforms the traditional algorithms in most benchmark problems, and its results is comparative to those obtained by the state-of-the-art algorithms. (C) 2015 Elsevier B.V. All rights reserved.
Genetic algorithm (GA) has been successfully applied for many numerical optimization problems in the history. Multi-parent genetic algorithm (MPGA) is an extended genetic algorithm which uses more than two parent as a...
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ISBN:
(纸本)9781509016365
Genetic algorithm (GA) has been successfully applied for many numerical optimization problems in the history. Multi-parent genetic algorithm (MPGA) is an extended genetic algorithm which uses more than two parent as a crossover operator for reproduction. Since MPGA has been increasing its interest in the family of genetic algorithms, it becomes an interesting algorithm to improve the solutions better than the traditional genetic algorithm by using the number of parents more than two for solving shuttle bus routing system (SBRS). In this paper, we compare MPGA and the traditional GA for the problem of SBRS in the Thammasat University (Rangsit Campus), Thailand. MPGA with up to 20 parents are used to optimize the shuttle bus routes in the campus. The diagonal crossover is used to measure the performance for both MPGA and GA in the reproduction process. The results prove that using multiple parents yields better solution than the traditional GA for solving the problem of SBRS.
In this paper, by using the unified procedures, an improved immune algorithm named a modified Taguchi-immune algorithm (MTIA), based on both the features of an artificial immune system and the systematic reasoning abi...
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In this paper, by using the unified procedures, an improved immune algorithm named a modified Taguchi-immune algorithm (MTIA), based on both the features of an artificial immune system and the systematic reasoning ability of the Taguchi method, is proposed to solve both the global numerical optimization problems with continuous variables and the combinatorial optimizationproblems for the job-shop scheduling problems (JSP). The MTIA combines the artificial immune algorithm, which has a powerful global exploration capability, with the Taguchi method, which can exploit the optimal antibody. In the MTIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. The systematic reasoning ability of the Taguchi method is executed in the recombination operations to select the better antibody genes to achieve the potential recombination, and consequently enhance the MTIA. The proposed MTIA is effectively applied to solve 15 benchmark problems of global optimization with 30 or 100 dimensions. The computational experiments show that the proposed MTIA can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing improved genetic algorithms reported recently in the literature. In addition, the MTIA is also applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. The computational experiments show that the proposed MTIA approach can also obtain both better and more robust results than those evolutionary methods reported recently. (c) 2007 Elsevier Inc. All rights reserved.
The Rosenbrock function is a well-known benchmark for numerical optimization problems, which is frequently used to assess the performance of Evolutionary Algorithms. The classical Rosenbrock function, which is a two-d...
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The Rosenbrock function is a well-known benchmark for numerical optimization problems, which is frequently used to assess the performance of Evolutionary Algorithms. The classical Rosenbrock function, which is a two-dimensional unimodal function, has been extended to higher dimensions in recent years. Many researchers take the high-dimensional Rosenbrock function as a unimodal function by instinct. In 2001 and 2002, Hansen and Deb found that the Rosenbrock function is not a unimodal function for higher dimensions although no theoretical analysis was provided. This paper shows that the n-dimensional (n = 4 similar to 30) Rosenbrock function has 2 minima, and analysis is proposed to verify this. The local minima in some cases are presented. In addition, this paper demonstrates that one of the "local minima" for the 20-variable Rosenbrock function found by Deb might not in fact be a local minimum.
The paper describes the application of the block-diagram, digital simulation system, SIDAS, for the purpose of designing control systems. The main features of the system are easy handling by the use of interactive gra...
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The paper describes the application of the block-diagram, digital simulation system, SIDAS, for the purpose of designing control systems. The main features of the system are easy handling by the use of interactive graphical input/output, operational flexibility through on-line modification of parameters and direct and convenient accesss to most of the computer's *** simulation system has access to external, user-written tasks. Thus, complex operations and devices which are presently not available, may be included in the simulation run. The operational modes and the possibilities of the simulation system will be demonstrated by examples.
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