Advances In Metaheuristics Research Articles

An improved Jaya optimization algorithm with Lévy flight

Recent advances in metaheuristics have shown the advantages of using the Lévy distribution, which models a kind of random walk (named “Lévy flight”) with occasional “big” steps. This characteristic makes Lévy flight especially useful for performing large “jumps” that allow the search to escape from a local optimum and restart in a different region of the search space. In this paper, we investigate this idea by applying Lévy flight to Jaya, a simple yet effective Swarm Intelligence optimization algorithm recently proposed in the literature. We perform experiments on the CEC 2014 benchmark as well as five industrial optimization problems taken from the CEC 2011 benchmark, and compare the performance of the proposed Lévy flight Jaya Algorithm (LJA) against several state-of-the-art algorithms for continuous optimization. Our numerical results show that, although both Jaya and LJA are in general less efficient than the most advanced algorithms on the CEC 2014 benchmark, LJA largely outperforms the original Jaya algorithm in most cases, and is also highly competitive on the tested industrial problems.

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

Metaheuristics are gaining increasing recognition in many research areas, computational systems biology among them. Recent advances in metaheuristics can be helpful in locating the vicinity of the global solution in reasonable computation times, with Differential Evolution (DE) being one of the most popular methods. However, for most realistic applications, DE still requires excessive computation times. With the advent of Cloud Computing effortless access to large number of distributed resources has become more feasible, and new distributed frameworks, like Spark, have been developed to deal with large scale computations on commodity clusters and cloud resources. In this paper we propose a parallel implementation of an enhanced DE using Spark. The proposal drastically reduces the execution time, by means of including a selected local search and exploiting the available distributed resources. The performance of the proposal has been thoroughly assessed using challenging parameter estimation problems from the domain of computational systems biology. Two different platforms have been used for the evaluation, a local cluster and the Microsoft Azure public cloud. Additionally, it has been also compared with other parallel approaches, another cloud-based solution (a MapReduce implementation) and a traditional HPC solution (a MPI implementation)

Special issue on recent advances in metaheuristics

Since 1995, the Metaheuristics International Conferences have been the meeting point of the leading researchers in the highly successful area of metaheuristics. The seventh of these conferences, MIC 2007, was held in Montreal, Canada, in June 2007 in honor of Fred Glover’s 70th birthday. The presence of more than 200 participants from all around the world and the more than 135 plenary and contributed talks that were presented reflected the extraordinary vitality of the field and the essential role of metaheuristics in addressing complex optimization problems. In connection with the activities of this conference, we are very glad to present this special issue of the Journal of Heuristics, devoted to Recent Advances in Metaheuristics. We sought submissions from the entire international research community in metaheuristics. Twenty-seven manuscripts were received and went through a strict review process, at the end of which twelve papers covering a broad spectrum of methodological and application topics in metaheuristics were selected for publication in this special issue. The first four papers of the special issue focus on new approaches that combine metaheuristics with other problem-solving paradigms to yield highly effective solution methods for difficult problems. In “Alternating Control Tree Search for Knap-

Advances in metaheuristics for gene selection and classification of microarray data

Gene selection aims at identifying a (small) subset of informative genes from the initial data in order to obtain high predictive accuracy for classification. Gene selection can be considered as a combinatorial search problem and thus be conveniently handled with optimization methods. In this article, we summarize some recent developments of using metaheuristic-based methods within an embedded approach for gene selection. In particular, we put forward the importance and usefulness of integrating problem-specific knowledge into the search operators of such a method. To illustrate the point, we explain how ranking coefficients of a linear classifier such as support vector machine (SVM) can be profitably used to reinforce the search efficiency of Local Search and Evolutionary Search metaheuristic algorithms for gene selection and classification.

Interfaces in Computer Science and Operations Research: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies

Interfaces in Computer Science and Operations Research: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies

Interfaces in Computer Science and Operations Research: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies.

Interfaces in Computer Science and Operations Research: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies.