Hybrid metaheuristic, an advancement over classical metaheuristic, provides a more effective search methodology. It combines several metaheuristic algorithms into one optimization mechanism. In this paper image enhancement is considered as an optimization problem. Hybrid metaheuristic techniques are used to find the optimum value for a set of parameters of a transformation function, with an aim...

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve ...

In recent years, several optimization methods especially metaheuristic optimization methods have been developed by scientists. People have utilized power of nature to solve problems. Therefore, those metaheuristic methods have imitated physical and biological processes of nature. In 2007, Big Bang Big Crunch optimization algorithm based on evolution of universe and in 2009, Gravitational Search...

A metaheuristic is generally a procedure designed to find a good solution to a difficult optimization problem. Known optimization search metaheuristics heavily rely on parameters, which are usually introduced so that the metaheuristic follows some supposedly related to the optimization problem natural process (simulated annealing, swarm optimization, genetic algorithms). Adjusting the parameter...

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales’ behavior of communicating with each other via ultras...

Despite their empirical effectiveness, our theoretical understanding of metaheuristic algorithms based on local search (and all other paradigms) is very limited, leading to significant problems for both researchers and practitioners. Specifically, the lack of a theory of local search impedes the development of more effective metaheuristic algorithms, prevents practitioners from identifying the ...

Cryptology is a thriving research area of great practical importance. It is a fundamental building block of communications security. Metaheuristic optimisation techniques such as simulated annealing and genetic algorithms have found successful application in a huge number of fields. However, their application to leading edge industrial-strength cryptology has been slight. The power of metaheuri...

The aim of this paper is to develop a hybrid metaheuristic based on Variable Neighbourhood Search and Tabu Search for solving the Feature Subset Selection Problem in classification. Given a set of instances characterized by several features, the classification problem consists of assigning a class to each instance. Feature Subset Selection Problem selects a relevant subset of features from the ...