For some real-world problems, it is desirable to find multiple global optima as many as possible. The multimodal optimization approach which finds multiple optima in a single run shows significant difference with the ...
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For some real-world problems, it is desirable to find multiple global optima as many as possible. The multimodal optimization approach which finds multiple optima in a single run shows significant difference with the single modal optimization *** whale optimization algorithm (WOA) is a newly emerging reputable optimization algorithm. Its global search ability has been verified in many benchmark functions and real-world applications. In this paper, we propose a multimodal version of whale optimization algorithm (MMWOA). MMWOA enhances the multimodal search ability of WOA by using the niching technique and improves the local search efficiency of WOA by combining the Gaussian sampling technique. The algorithm has been tested on multimodal optimization benchmark functions recommended by CEC'2013 and on a multimodal optimization problem with non-linear constraints. Experimental results indicate that MMWOA has competitive performance compared with other state-of-the-art multimodal optimization algorithms.
Many real world optimization problems turn out to be multi-objective optimization problems revealing a remarkable number of locally optimal solutions corresponding to the chosen objective function. Therefore, it seems...
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Many real world optimization problems turn out to be multi-objective optimization problems revealing a remarkable number of locally optimal solutions corresponding to the chosen objective function. Therefore, it seems desirable to detect as many of those solutions with as few objective function calls as possible. A niching Higher Order Evolution Strategy (NES) can successfully be applied to locate a large number of these local solutions during a single optimization run. Additionally, it turns out that all of these solutions can be found next to the front of non-dominated solutions. Therefore, evaluating more than one objective function (in parallel or in series) yields a good approximation of the Pareto-optimal front. The proposed method will be tested against several test functions and then applied to the solution of a magnetic shunting problem.
A particle swarm optimization model for tracking multiple peaks over a multimodal fitness landscape is described here. Multimodal optimization amounts to finding multiple global and local optima (as opposed to a singl...
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ISBN:
(纸本)9783642271717
A particle swarm optimization model for tracking multiple peaks over a multimodal fitness landscape is described here. Multimodal optimization amounts to finding multiple global and local optima (as opposed to a single solution) of a function, so that the user can have a better knowledge about different optimal solutions in the search space. niching algorithms have the ability to locate and maintain more than one solution to a multi-modal optimization problem. The Particle Swarm Optimization (PSO) has remained an attractive alternative for solving complex and difficult optimization problems since its advent in 1995. However, both experiments and analysis show that the basic PSO algorithms cannot identify different optima, either global or local, and thus are not appropriate for multimodal optimization problems that require the location of multiple optima. In this paper a niching algorithm named as Modified Local Neighborhood Based niching Particle Swarm Optimization (ML-NichePSO)is proposed. The ability, efficiency and usefulness of the proposed method to identify multiple optima are demonstrated using well-known numerical benchmarks.
In this paper, for the first time, multi-modal genetic algorithms (MMGAs) are proposed to optimize the resource constrained multi-project scheduling problem (RCMPSP). In problems where the landscape has both multiple ...
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In this paper, for the first time, multi-modal genetic algorithms (MMGAs) are proposed to optimize the resource constrained multi-project scheduling problem (RCMPSP). In problems where the landscape has both multiple local and global optima, such as the RCMPSP, a MMGAs approach can provide managers with an advantage in decision-making because they can choose between alternative solutions equally good. Alternative optima are achieved because the diversification techniques of MMGAs introduce diversity in population, decreasing the possibility of the optimization process getting caught in a unique local or global optimum. To compare the performance of a MMGAs approach with other alternative approaches, commonly accepted by researchers to solve the RCMPSP such as classical genetic algorithms and dispatching heuristics based on priority rules, we analyse two time-based objective functions (makespan and average percent delay) and three coding systems [random keys (RK), activity list (AL), and a new proposal called priority rule (PR)]. We have found that MMGAs significantly improve the efficacy (the algorithm's capability to find the best optimum) and the multi-solution-based efficacy (the algorithm's capability to find multiple optima) of the other two approaches. For makespan the PR is the best code in terms of the efficacy and multi-solution-based efficacy, and the RK is the best code for the average percent delay.
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