Metaheuristics are known to be strong in solving large-scale instances of computationally hard problems. However, their efficiency still needs exploration in the context of instance structure, scale and numerical properties for many of these problems. In this paper, we present an in-depth computational study of two local search metaheuristics for the classical uncapacitated facility location pr...

Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majorit...

Thresholding is an important approach for image segmentation and it is the first step in the image processing for many applications. Segmentation is a low level operation that can segment an image in nonoverlapping regions. The optimal thresholds are found by maximizing Kapur's entropy-based thresholding function in a grey level image. However, the required CPU time increases exponentially with...

This paper considers the problem of multi factory parallel machine problems. This problem is already studied in the literature and there is a mathematical model and a genetic algorithm for the problem. We analyzed the model and algorithm, and showed that they suffer from serious shortcomings. Then, we propose three mathematical models for makespan and total completion time objectives. The propo...

The main goal of this paper is to attempt an unbiased comparison of the performance of straightforward implementations of five different metaheuristics on a university course timetabling problem. In particular, the metaheuristics under consideration are Evolutionary Algorithms, Ant Colony Optimization, Iterated Local Search, Simulated Annealing, and Tabu Search. To attempt fairness, the impleme...

For many applications of vehicle routing, there is still a large gap between the requirements and the performance of today’s decision support systems. Although there has been a tremendous increase in the ability to solve ever more complex VRPs (partly due to methodological improvements, partly due to the general increase in computing power), the ability to consistently provide better routing pl...

Metaheuristics are renowned to present very efficient elucidation to many of today's combinatorial optimization problems in engineering, industrial, economical and scientific domains such as transportation, bioinformatics, logistics, business etc. Scheduling, timetabling, vehicle routing, resource allocation are intelligently and successfully tackled with Metaheuristic approaches such as Simula...

This special issue emerged from contributions presented at the 10th Metaheuristics International Conference (MIC), held at the Singapore Management University in Singapore, August 5–8, 2013. The first MIC took place in Breckenridge, Colorado, USA in 1995. Due to its success, the conference was established as a bi-annual event that gained further attraction and grew in size steadily. Today, MIC ...

Metaheuristics are a class of effective algorithms for optimization problems. A basic implementation of a metaheuristic typically requires rather little development effort. With a significantly larger investment in the design, implementation, and fine-tuning, metaheuristics can often produce state-of-the-art results. According to the amount of development effort, we say that an implementation o...