The effective maintenance scheduling of power system generators is very important for the economical and reliable operation of a power system. his represents a tough scheduling problem which continues to present a challenge for efficient optimization solution techniques. This paper resents the application of metaheuristic approaches, such as a genetic algorithm (GA), simulated annealing (SA) an...

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining th...

Spatial developmental programs in Hungary are important to support economically lagging regions. As a result of their nature they can be considered as big projects, with long timescales, high levels of capital, causing significant impact on the targeted field. For an effective performance, an accurate planning phase is essentially important. In this paper we concentrate on the project schedulin...

Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and ...

The classical vehicle routing problems are designed for distance or cost reduction. The routes generated by the model will be insensitive towards the environmental impact. In this work, a green vehicle routing problem is addressed. A metaheuristic algorithm combining an Ant Colony Optimization algorithm with a Variable Neighbourhood Search algorithm is developed to solve the problem. The hybrid...

The Graph Partitioning Problem (GPP) is one of the most studied NPcomplete problems notable for its broad spectrum of applicability such as in VLSI design, data mining, image segmentation, etc. Due to its high computational complexity, a large number of approximate approaches have been reported in the literature. Hybrid algorithms that are based on adaptations of popular metaheuristic technique...

RNNs have local feedback loops within the network which allows them to shop earlier accessible patterns. This network can be educated with gradient descent back propagation and optimization technique such as second-order methods; conjugate gradient, quasi-Newton, Levenberg-Marquardt have also been used for networks training [14, 15]. But still this algorithm is not definite to find the global m...

The Mirrored Traveling Tournament Problem (mTTP) is an optimization problem that represents certain types of sports timetabling, where the objective is to minimize the total distance traveled by the teams. This work proposes the use of hybrid heuristic to solve the mTTP, using an evolutionary algorithm in association with the metaheuristic Simulated Annealing. It suggests the use of Genetic Alg...