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During the recent decades, many niching methods have been proposed and empirically verified on some available test problems. They often rely on some particular assumptions associated with the distribution, shape, and size of the basins, which can seldom be made in practical optimization problems. This study utilizes several existing concepts and techniques, such as taboo points, normalized Mahalanobis distance, and the Ursem's hill-valley function in order to develop a new tool for multimodal optimization, which does not make any of these assumptions. In the proposed method, several subpopulations explore the search space in parallel. Offspring of a subpopulation are forced to maintain a sufficient distance to the center of fitter subpopulations and the previously identified basins, which are marked as taboo points. The taboo points repel the subpopulation to prevent convergence to the same basin. A strategy to update the repelling power of the taboo points is proposed to address the challenge of basins of dissimilar size. The local shape of a basin is also approximated by the distribution of the subpopulation members converging to that basin. The proposed niching strategy is incorporated into the covariance matrix self-adaptation evolution strategy (CMSA-ES), a potent global optimization method. The resultant method, called the covariance matrix self-adaptation with repelling subpopulations (RS-CMSA), is assessed and compared to several state-of-the-art niching methods on a standard test suite for multimodal optimization. An organized procedure for parameter setting is followed which assumes a rough estimation of the desired/expected number of minima available. Performance sensitivity to the accuracy of this estimation is also studied by introducing the concept of robust mean peak ratio. Based on the numerical results using the available and the introduced performance measures, RS-CMSA emerges as the most successful method when robustness and efficiency are considered at the same time.

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Inspired by the lateral line of aquatic vertebrates, an artificial lateral line (ALL) system can localize and track an underwater moving object by analyzing the ambient flow caused by its motion. There are several studies on object detection, localization and tracking by ALL systems, but only a few have investigated the optimal design of the ALL system, the one that on average provides the highest characterization accuracy. Design optimization is particularly important because the uncertainties in the employed flow model and in sensor measurements deteriorate the reliability of sensing. This study investigates the optimal design of the ALL system in three-dimensional (3D) space for dipole source characterization. It highlights some challenges specific to the 3D setting and demonstrates the shortcomings of the designs in which all sensors and their sensing directions are in the same plane. As an alternative, it proposes two design concepts, called 'Offset Strategy' and 'Angle Strategy' to overcome these shortcomings. It investigates potentials of having a swarm of cooperative ALLs as well. It performs design optimization in the presence of sensor and model uncertainties and analyzes the trade-off between the number of sensors and characterization accuracy. The obtained solutions are analyzed to reveal their strategies in solving the problem efficiently. The dependency of the optimized solutions on the uncertainties is also demonstrated.

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