Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief hi...

Taking inspiration from the brain, spiking neural networks (SNNs) have been proposed to understand and diminish gap between machine learning neuromorphic computing. Supervised is most commonly used algorithm in traditional ANNs. However, directly training SNNs with backpropagation-based supervised methods challenging due discontinuous non-differentiable nature of neuron. To overcome these probl...

The failure probability of the structures is one of the challenging problems in structural engineering. To obtain the reliability index introduced by Hasofer and Lind, one needs to solve a nonlinear equality constrained optimization problem. In this study, four of the most recent metaheuristic algorithms are utilized for finding the design point and the failure probability of problems with cont...

The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the parameters of heuristi...

The metaheuristics are approximation methods which deal with difficult optimization problems. The Work that we present in this paper has primarily as an objective the adaptation and the implementation of two advanced metaheuristics which are the Memetic Algorithms (MA) and the Electromagnetism Metaheuristic (EM) applied in the production systems of Hybrid Flow Shop (HFS) type for the problem of...

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the AC...