In this paper the performance of four well-known metaheuristics consisting of Artificial Bee Colony (ABC), Biogeographic Based Optimization (BBO), Harmony Search (HS) and Teaching Learning Based Optimization (TLBO) are investigated on optimal domain decomposition for parallel computing. A clique graph is used for transforming the connectivity of a finite element model (FEM) into that of the cor...

Over the last years, so-called hybrid optimization approaches have become increasingly popular for addressing hard optimization problems. In fact, when looking at leading applications of metaheuristics for complex real-world scenarios, many if not most of them do not purely adhere to one specific classical metaheuristic model but rather combine different algorithmic techniques. Concepts from di...

Recent work in combinatorial optimization indicates the high potential of combining metaheuristics with integer linear programming (ILP) techniques. We study here a hybrid system in which a memetic algorithm (MA) and a general purpose ILP solver based on branch-and-cut (B&C) are executed in parallel and continuously exchange information in a bidirectional, asynchronous way. As target problem, w...

increasing of the distribution efficiency is one of the most objectives of an integrated logistic system developed as a new management philosophy in the past few decades. the problem is examind in two parts: facilities location problem (flp) for long policies and vehicle routing problem (vrp) to meet the customer demand. these two components can be solved separately; however, this solution may ...

Accurate parameter estimation of solar cells is vital to assess and predict the performance of photovoltaic energy systems. For the estimation model to accurately track the experimentally measured current-voltage (I-V) data, the parameter estimation problem is converted into an optimization problem and a metaheuristic optimization algorithm is used to solve it. Metaheuristics present a fairly a...

A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND ...

The objective of vehicle routing problem (VRP) is to design a set of vehicle routes in which a fixed fleet of delivery vehicles from one or several depots to a number of customers have to be set with some constraints. To this date in the literature, many instances of VRP model have been introduced and applied for various types of scheduling problems. However, when implemented in a real life app...

In this thesis we focus on Stochastic Combinatorial Optimization Problems (SCOPs), a wide class of combinatorial optimization problems under uncertainty, where part of the information about the problem data is unknown at the planning stage, but some knowledge about its probability distribution is assumed. Optimization problems under uncertainty are complex and difficult, and often classical alg...

Solution of Abstract Optimization problems with two or more conflicting functions or objectives by using metaheuristics has attracted attention of researches and become a rapidly developing area known as Multiobjective Optimization. Metaheuristics are non-exact techniques aimed to produce satisfactory solutions to complex optimization problems where exact techniques are not applicable; they are...

In recent years, much attention has been devoted to the development of metaheuristics and local search algorithms for tackling stochastic combinatorial optimization problems. In this paper, we propose an effective local search algorithm that makes use of empirical estimation techniques for a class of stochastic combinatorial optimization problems. We illustrate our approach and assess its perfo...