Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired b...

In this paper we introduce a constrained hybrid PSO algorithm, called Improved Hybrid Particles Swarm Optimization (I-HPSO), in order to find feasible solutions to the problem of resource allocation in the downlink of a multi-cell WCDMA system. An economic model is developed that considers the utility of the provided service, the acceptance probability of the service by the users and the revenu...

Solving efficiently large benchmarks of NP-hard permutation-based problems requires the development of hybrid methods combining different classes of optimization methods. Indeed, it is now acknowledged that such methods perform better than traditional optimization methods when used separately. The key challenge is how to find connections between the divergent search strategies used in each clas...

Solving efficiently large benchmarks of NP-hard permutation-based problems requires the development of hybrid methods combining different classes of optimization methods. Indeed, it is now acknowledged that such methods perform better than traditional optimization methods when used separately. The key challenge is how to find connections between the divergent search strategies used in each clas...

Population based metaheuristics, such as hybrid genetic algorithms or memetic algorithms, play an important role in the solution of combinatorial optimization problems. The memetic algorithm presented here for the no-wait flowshop scheduling problem addresses a hierarchically organized complete ternary tree to represent the population that putted together with a recombination plan resembles a p...

Heuristic algorithms for solving the task scheduling problem with moving executors to minimize the sum of completion times are considered. The corresponding combinatorial optimization problem is formulated. Three hybrid solution algorithms are introduced. As a basis an evolutionary algorithm is assumed that is combined with the procedure that uses simulated annealing metaheuristics. The results...

Modern search methods for optimization consider hybrid search metaheuristics those employing general optimizers working together with a problem-specific local search procedure. The hybridism comes from the balancing of global and local search procedures. A challenge in such algorithms is to discover efficient strategies to cover all the search space, applying local search only in actually promi...

many real world problems can be modelled as an optimization problem. evolutionary algorithms are used to solve these problems. ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. these ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. ant colony optim...

The ultimate goal of designing a symmetric-key cryptographic primitive often can be formulated as an optimization problem. So far, these problems mainly have been solved with trivial algorithms such as brute force or random search. We show that a more advanced and equally versatile class of search algorithms, called metaheuristics, can help to tackle optimization problems related to design of s...

This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunction with other metaheuristics, managing the implementation of local search algorithms for optimization problems. Usually the local search is costly and should be used only in promising regions of the search space. The CS assists in the discovery of these regions by dividing the search space into...