This paper describes a new metaheuristic technique to solve the travelling salesman problem based on particle swarm using guided local search. The main contribution of the paper is that is develops a particle swarm-based strategy to solve combinatorial optimization problems in general, and the TSP in particular. It first establishes the steps to be followed in the new method and then goes on to...

Hard optimization problems that cannot be solved within reasonable time by standard, mathematical, deterministic methods are of great practical interest. Metaheuristics inspired by nature were recently successfully used for such problems. These metaheuristics are based on random Monte-Carlo search guided by simulation of some nature intelligence, especially evolution and swarm intelligence. One...

Several methods exist for breaking symmetry in constraint problems, but most potentially suffer from high memory requirements, high computational overhead, or both. We describe a new partial symmetry breaking method that can be applied to arbitrary variable/value symmetries. It models dominance detection as a nonstationary optimisation problem, and solves it by resource-bounded metaheuristic se...

Software testing is one of the most labor-intensive and expensive phase of the software development life cycle. Software testing includes test case generation and test suite optimization that has a strong impact on the effectiveness and efficiency of software testing. Over the past few decades, there has been active research to automate the process of test case generation but the attempts have ...

Evolutionary methods when used as a test data generator optimize the given input (usually called test case) according to a selected test coverage criterion encoded as a fitness function. Basically, the genetic algorithms and other Evolutionary techniques are based on pure random search. However, these algorithms adapt to the given problem. In the last decade lot of evolution based metaheuristic...

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...

A metaheuristic is an intelligent, iterative process that guides a search and can be applied towards optimization problems, such as the Traveling Salesman Problem. Two well studied techniques for solving optimization problems are Genetic Algorithms and Ant Colony Systems. However, each metaheuristic has different strengths and weaknesses. Genetic Algorithms are able to quickly find near optimal...

Abstract The power systems are important by using short term load forecasting (STLF) because it predicts the in 24 hours ahead or a week ahead. artificial neural network (ANN) brings good result predicted of its accurateness, easiness processing data, construction model as well excellent performances. optimization value ANN is found different methods which consist some weights. This manuscript ...

In this paper a metaheuristic algorithm composed of particle swarm, ray optimization, and harmony search (HRPSO) is presented for optimal design of truss structures. This algorithm is based on the particle swarm ray origin making is used to update the positions of the particles, and for enhancing the exploitation of the algorithm the harmony search is utilized. Numerical results demonstrate the...