Experience has shown that a crafted combination of concepts of different metaheuristics can result in robust combinatorial optimization schemes and produce higher solution quality than the individual metaheuristics themselves, especially when solving difficult real-world combinatorial optimization problems. This chapter gives an overview of different ways to hybridize GRASP (Greedy Randomized A...

The fuel consumption of a simulation model of a real Hybrid Electric Vehicle is optimized on a standardized driving cycle using metaheuristics (PSO, ES, GA). Search space discretization and metamodels are considered for reducing the number of required, time-expensive simulations. Two hybrid metaheuristics for combining the discussed methods are presented. In experiments it is shown that the use...

We provide a categorized bibliography of metaheuristics for solving the vehicle routing problem and its extensions. The categories are based on various types of metaheuristics and vehicle routing problems

Following decades of sustained improvement, metaheuristics are one the great success stories optimization research. However, in order for research to avoid fragmentation and a lack reproducibility, there is pressing need stronger scientific computational infrastructure support development, analysis comparison new approaches. We argue that, via principled choice support, field can pursue higher ...

Metaheuristics are truly diverse in nature — under the overarching theme of performing operations to escape local optima (we assume minima in this chapter without loss of generality), algorithms as different as ant colony optimization ([12]), tabu search ([16]), and genetic algorithms ([23]) have emerged. Due to the unique functionality of each type of metaheuristic, comparison of metaheuristic...

To optimally solve hard optimization problems in real life, many methods were designed and tested. The metaheuristics proved to be the generally adequate techniques, while the exact traditional optimization mathematical methods are prohibitively expensive in computational time. The population-based metaheuristics, which manipulate a set of candidate solutions at a time, have advantages over the...

Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search o...

This report discusses two different approaches to the description of metaheuristics. On one hand, we propose a number of different high-level criteria according to which metaheuristics can be described and classified. On the other hand, we discuss some method of design of experiments for studying the contribution and the relative importance of the different components of a metaheuristic. We mai...

In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems (MOPs) has become an active research area. Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front and uniform diversity. Most studies on metahe...

This chapter reviews approaches where metaheuristics are used to boost the performance of exact integer linear programming (IP) techniques. Most exact optimization methods for solving hard combinatorial problems rely at some point on tree search. Applying more effective metaheuristics for obtaining better heuristic solutions and thus tighter bounds in order to prune the search tree in stronger ...