We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of...

Combinatorial optimization problems are those problems that have a finite set of possible solutions. The best way to solve a combinatorial optimization problem is to check all the feasible solutions in the search space. However, checking all the feasible solutions is not always possible, especially when the search space is large. Thus, many meta-heuristic algorithms have been devised and modifi...

The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to lim...

In this paper the recently developed meta-heuristic optimization method, known as charged system search (CSS), is utilized for optimum nodal ordering to minimize bandwidth and profile of sparse matrices. The CSS is an optimization algorithm, which is based on the governing laws of Coulomb and Gauss from electrostatics and the Newtonian mechanics of motion. The bandwidth and profile of some grap...

Meta-heuristics are high-level approaches developed to discover a heuristic that provides reasonable solution many varieties of optimization problems. The classification problems contain sort problem. Simply, the objective herein is reduce number misclassified instances. In this paper, question whether meta-heuristic methods can be used construct linear models or not answered. To end, Particle ...

In recent decades, with the introduction of optimization problems, new methods of was optimizing developed. The most important group of optimization techniques is meta-heuristic method. That is able to solve the problems of combination optimizing. The major problems in the combination optimizing such as Dynamic Travelling Salesman Problem (DTSP) is a kind of problems that is close answer to the...

In this study, topology optimization is applied to concentrically braced frames in order to find economical solutions for conventional structural steel frames. Differential Evolution Algorithm and Dolphin Echolocation Optimization are applied for structural optimization. Numerical examples are studied and results of comparison with other meta-heuristic algorithms, including Genetic Algorithm, A...

Predictive maintenance scheduling is an optimization problem aimed at defining the best activity sequence to minimize the expected cost over a time horizon. For very-large systems such as in experimental physics, maintenance optimization turns out to be very difficult owing to analytically intractable objective functions. In this paper, a meta-heuristic predictive maintenance algorithm based on...

Structural optimization with frequency constraints is a challenging class of optimization problems characterized by highly non-linear and non-convex search spaces. When using a meta-heuristic algorithm to solve a problem of this kind, exploration/exploitation balance is a key feature to control the performance of the algorithm. An excessively exploitative algorithm might focus on certain areas ...

This paper presents an efficient meta-heuristic algorithm for optimization of double-layer scallop domes subjected to earthquake loading. The optimization is performed by a combination of harmony search (HS) and firefly algorithm (FA). This new algorithm is called harmony search firefly algorithm (HSFA). The optimization task is achieved by taking into account geometrical and material nonlinear...