The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many
signal processing and machine learning (ML) applications. It is used for solving optimization …

This paper presents a generic strategy for short-term load forecasting (STLF) based on the
support vector regression machines (SVR). Two important improvements to the SVR based …

In this study, a new single-solution based metaheuristic, namely the Vortex Search (VS)
algorithm, is proposed to perform numerical function optimization. The proposed VS …

The simplicial homology global optimisation (SHGO) algorithm is a general purpose global
optimisation algorithm based on applications of simplicial integral homology and …

This manuscript reviews recent advances in deterministic global optimization for Mixed-
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …

In practical applications of optimization it is common to have several conflicting objective
functions to optimize. Frequently, these functions are subject to noise or can be of black-box …

The adaptive momentum method (AdaMM), which uses past gradients to update descent
directions and learning rates simultaneously, has become one of the most popular first-order …

Thanks to their simplicity and flexibility, evolutionary algorithms (EAs) have attracted
significant attention to tackle complex optimization problems. The underlying idea behind all …

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