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This paper compares three modern and two classical multiobjective optimizers (MOOs) as applied to real-world problems in electromagnetics. The behavior of sophisticated optimizers on simple test functions has been studied exhaustively. In contrast, the algorithms here are tested on practical applications, where the function evaluations are computationally expensive, making the convergence rate a crucial factor. The examples considered include the optimization of a narrowband slot antenna, a mushroom-type electromagnetic bandgap structure, and an ultrawideband Vivaldi antenna. Another popular topic in the literature is in comparing classical MOOs on electromagnetics problems. The modern optimizers chosen in this paper are state of the art and each has a distinct design philosophy. This paper introduces two unique MOOs to the electromagnetics community: BORG, an auto-adaptive genetic algorithm and the Multi-Objective Covariance Matrix Adaptation Evolutionary Strategy (MO-CMA-ES), an extension of the popular single-objective CMA-ES. These algorithms are compared to the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), a Chebysheff scalarization algorithm, and two classical MOOs. This paper will study the behavior of these algorithms on problems in electromagnetics with a limited number of function evaluations using five distinct metrics and will provide useful guidelines and recommended optimizer settings. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid genetic algorithm (GA) for the optimal shape design of an axially symmetric dual-reflector antenna by combining the GA with the moving least square (MLS), which enhances the convergence rate and the global search performance. The MLS is used to construct local interpolation functions from non-uniform sample data and to estimate new superior positions. By combining these superior positions in the next generation, the MLS-GA shows better search performance for the global optimum and a faster convergence rate than those of the conventional GA. To verify the proposed MLS-GA, we applied it to the optimal shape design of the DRA at the W-band. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm based on the genetic algorithm (GA) and the evolution strategy (ES) for the electromagnetic optimization problem. The GA is not good enough at times in searching the optimal solution from the view point of the convergence speed and the solution quality, while the ES has the risk of being trapped in a local minimum. The hybrid algorithm is composed of GA and ES in order to make up for these defects. First, we reached the vicinity of optimal solution using the GA. Then, the ES is used to find the accurate optimal solution. The switching point can be a main issue, which is also resolved in this paper. First, the performance of the convergence speed and the solution accuracy are comparatively tested using the known functions. In addition, the optimized design of the 2.45 GHz coplanar waveguide-fed circularly polarized antenna is carried out as a practical application. Only the GA and the hybrid algorithm reach the satisfactory value, and the more rapid convergence can be shown by the ES in this hybrid method after 380 iterations. [ABSTRACT FROM AUTHOR]

Distribution system reconfiguration (DSR) is a multi-objective, nonlinear problem. This paper introduces a new, fast, nondominated sorting genetic algorithm (FNSGA) for the purpose of solving the DSR problem in normal operation by satisfying all objectives simultaneously with a relatively small number of generations and relatively short computation time. The objectives of the problem are to minimize real power losses and improve the voltage profile and load balancing index with minimum switching operations. Instead of generating several ranks from the nondominated set of solutions, this algorithm deals with only one rank; then the most suitable solution is chosen according to the operator's wishes. If there is no preference and all objectives have the same degree of importance, the best solution is determined by simply considering the sum of the normalized objective values. Also, a guided mutation operation is applied instead of a random one to speed up convergence. Radial system topology is satisfied using graph theory by formulating the branch-bus incidence matrix (BBIM) and checking the rank of each topology. To test the algorithm, it was applied to two widely studied test systems and a real one. The results show the efficiency of this algorithm as compared to other methods in terms of achieving all the goals simultaneously with reasonable population and generation sizes and without using a mutation rate, which is usually problem-dependent. [ABSTRACT FROM PUBLISHER]