Practical optimization problems are often too complex to be formulated exactly. Knowing multiple good alternatives can help decision-makers easily switch solutions when needed, such as faced with unforeseen constraints. A multimodal task aims find global optima well high-quality local of an problem. Evolutionary algorithms niching techniques commonly used for problems, where a rough estimate th...

AbstractReliable uncertainty quantification in deep neural networks is very crucial safety-critical applications such as automated driving for trustworthy and informed decision-making. Assessing the quality of estimates challenging ground truth not available. Ideally, a well-calibrated model, should perfectly correlate with model error. We propose novel error aligned optimization method introdu...

In this study, an improved gbest-PSO is proposed to overcome the shortcoming of earlier convergence of classical gbest-PSO. Then the improved gbest-PSO is used to identify the unknown inlet temperature profile in a plate channel flow. The effects of measurement position and measurement error on the accuracy of prediction are studied thoroughly. Analysis of computational results of two test prob...

Methods to deal with Multimodal Optimization Problems (MMOP) can be classified in three main approaches, regarding the number and the type of desired solutions. In general, methods can be applied to find: (1) only one global solution; (2) all global solutions; and (3) all local solutions of a given MMOP. The simultaneous capture of several solutions of MMOPs without parameter adjustment is stil...

This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into t...