Abstract
The memetic algorithms which employ population information spreading mechanism have shown great potentials in solving complex three-dimensional black-box problems. In this paper, a newly developed memetic meta-heuristic optimization method, known as shuffled frog leaping algorithm (SFLA), is modified and applied to topology optimization of electromagnetic problems. Compared to the conventional SFLA, the proposed algorithm has an extra local search step, which allows it to escape from the local optimum, and hence avoid the problem of premature convergence to continue its search for more accurate results. To validate the performance of the proposed method, it was applied to solving the topology optimization of an interior permanent magnet motor. Two other EAs, namely the conventional SFLA and local-search genetic algorithm, were applied to study the same problem and their performances were compared with that of the proposed algorithm. The results indicate that the proposed algorithm has the best trade-off between the results of objective values and optimization time, and hence is more efficient in topology optimization of electromagnetic devices.
Highlights
Topology optimization (TO) is the process of determining the material distribution for devices that could yield the best performance under given constraints
Based on the optimization searching methodologies, these methods can be categorized as the deterministic method, where the searching process is based on the gradient information, such as the Newton method; and the meta-heuristic method, where the searching process is guided by the information gathered from random or designed population, such as the evolutionary algorithms, or those from the related disciplines such as the bidirectional evolution structural optimization algorithm (BESO) [3,4]
Both the speed and accuracy of the algorithms were compared, and the results indicate that the proposed shuffled frog leaping algorithm (SFLA) is more appropriate in the design and optimization of complicated electromagnetic devices such as interior permanent-magnet motor (IPM)
Summary
Topology optimization (TO) is the process of determining the material distribution for devices that could yield the best performance under given constraints. One issue with respect to the conventional EAs is the passage of genes is limited to the offsprings, because the generation of children is based on the crossover of elite parents only, and the searching direction is limited, resulting in a relatively poor searching efficiency To overcome this issue, local search steps are often added to the elite population or to the best candidate [9]. In the local search step, the neighborhood of the feasible candidate is searched by adding randomly generated vectors to the candidate and with their performances investigated Another type of meta-heuristic algorithms is the memetic algorithm, where memetic evolution is used as the searching mechanism. To further improve the efficiency of the SFLA and avoid the problem of finding false optima, a local search step is added to the algorithm to add randomness to the optimization process. Both the speed and accuracy of the algorithms were compared, and the results indicate that the proposed SFLA is more appropriate in the design and optimization of complicated electromagnetic devices such as IPMs
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