this paper addresses the vehicle routing problem with delivery time cost. this problem aims to find a set of routes of minimal total costs including the travelling cost and delivery time cost, starting and ending at the depot, in such a way that each customer is visited by one vehicle given the capacity of the vehicle to satisfy a specific demand. in this research, a hybrid metaheuristic approa...

Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests needed. However, choosing an appropriate test is not trivial, given that each has some assumptions about the distribution of underlying data must be true before it can used. Permutation (P-Tests) with minimal number assumptions. These simple, intuitive and nonparametric. this paper, we a...

Hybrid metaheuristic, an advancement over classical metaheuristic, provides a more effective search methodology. It combines several metaheuristic algorithms into one optimization mechanism. In this paper image enhancement is considered as an optimization problem. Hybrid metaheuristic techniques are used to find the optimum value for a set of parameters of a transformation function, with an aim...

Computational cost of metaheuristic based optimum design algorithms grows excessively with structure size. This results in computational inefficiency of modern metaheuristic algorithms in tackling optimum design problems of large scale structural systems. This paper attempts to provide a computationally efficient optimization tool for optimum design of large scale steel frame structures to AISC...

Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and ...

In recent years, several optimization methods especially metaheuristic optimization methods have been developed by scientists. People have utilized power of nature to solve problems. Therefore, those metaheuristic methods have imitated physical and biological processes of nature. In 2007, Big Bang Big Crunch optimization algorithm based on evolution of universe and in 2009, Gravitational Search...

This work proposes a metaheuristic-based approach to hyperparameter selection in multilayer perceptron classify EMG signals. The main goal of the study is improve performance model by optimizing four important hyperparameters: number neurons, learning rate, epochs, and training batches. proposed this shows that optimization using particle swarm gray wolf optimizer significantly improves classif...

Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired b...

logistic network design is one of the most important strategic decisions in supply chain management that has recently attracted the attention of many researchers. transportation network design is then one of the most important fields of logistic network. this study is concerned with designing a multi-stage and multi-product logistic network. at first, a mixed integer nonlinear programming model...