Most real-world search and optimization problems involve complexities such as non-convexity, nonlinearities, discontinuities, mixed nature of variables, multiple disciplines and large dimensionality, a combination of which renders classical provable algorithms to be either ineffective, impractical or inapplicable. There do not exist any known mathematically motivated algorithms for finding the ...

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...

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineer...

This paper describes the parallelization of a two-phase metaheuristic for the vehicle routing problem with time windows and a central depot (VRPTW). The underlying objective function combines the minimization of the number of vehicles in the first search phase of the metaheuristic and the minimization of the total travel distance in the second search phase. The parallelization of the metaheuris...

In this work we use metaheuristic search to improve Google’s Guava library, finding a semantically equivalent version of com.google. common.collect.ImmutableMultimap with reduced energy consumption. Semantics-preserving transformations are found in the source code, using the principle of subtype polymorphism. We introduce a new tool, Opacitor, to deterministically measure the energy consumption...

The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test generation process have been limited, having been constrained by the size and complexity of software, and the basic fact that in general, test data generation is an undecidable problem. Metaheuristic sear...

using advanced techniques of econometrics and a metaheuristic optimization approach, this study attempts to evaluate the potential advantages of international portfolio diversification for east asian international investors when investing in the middle eastern emerging markets. overall, the results of both econometric and the metaheuristic optimization methods are supporting each other. finding...

This paper demonstrates that metaheuristic algorithms can provide a useful general framework for estimating both linear and nonlinear econometric models. Two metaheuristic algorithms—firefly and accelerated particle swarm optimization—are employed in the context of several quantile regression models. The algorithms are stable and robust to the choice of starting values and the presence of vario...

This paper introduces a cooperative parallel metaheuristic for solving the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information id...

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...