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In this work, a new multi-objective population-based optimization algorithm is presented and tested. In this contribution, the concepts of fast non-dominating sorting and density estimation using the crowding distance are used to create a multi-objective optimization algorithm based on previous work, which is a single objective evolutionary optimization algorithm based on self-organizing maps (SOMs). The SOMs paradigm introduces a strong collaboration between neighbors solutions that improves exploitation. Furthermore, the representative power of the SOMs enhances the exploration and diversification. A state of the art benchmark approach is used to evaluate the performance of the proposed algorithm, obtaining positive results. The test problem uses an analytical model of an inductively coupled wireless power transfer system (WPT). The objective is to optimize the WPT model characteristics in order to allow simultaneous data and power transfer between the coils. The WPT design approach uses more degrees of freedom than existing techniques leading to a number of solutions where both the power signals and the data signal can coexist on the same physical channel achieving good figures of merit. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization. [ABSTRACT FROM AUTHOR]

A novel evolutionary algorithm is used for the optimization of the thrust force of the stages of an electromagnetic aircraft launch system, based on a double-sided tubular linear induction motor. A semianalytical model allows for a fast and accurate prediction of all the electromagnetic quantities in the device, including the thrust force, the back electromotive force, the distribution of the induced current density, and the average magnetic flux density in the teeth. Using the semianalytical formulation, the characterization of the machine is greatly facilitated, so allowing a fast evaluation of the cost function and of the design constraints. [ABSTRACT FROM PUBLISHER]