Abstract The Social Golfer Problem (SGP) is a combinatorial optimization problem that exhibits a lot of symmetry and has recently attracted significant attention. In this paper, we present a new greedy heuristic for the SGP, based on the intuitive concept of freedom among players. We use this heuristic in a complete backtracking search, and match the best current results of constraint solvers f...

The rapid increase of dimensions and complexity of real life problems makes it more difficult to find optimal solutions by traditional optimization methods. This challenge requires intelligent and sophisticated algorithms to make the right decisions given a set of inputs and a variety of possible actions. In the problem solving arena, this definition is transformed into the term of artificial i...

This work proposes an attempt to control the navigation of a wall following mobile robot using a hybrid metaheuristic which is a combination of two different population-based heuristics such as Gravitational Search (GS) and Particle Swarm Optimization (PSO). The hybrid metaheuristic is called GSPSO which is used to train a multi-layered Artificial Neural Network (ANN) with the available benchma...

One important approach to learning Bayesian networks (BNs) from data uses a scoring metric to evaluate the fitness of any given candidate network for the data base, and applies a search procedure to explore the set of candidate networks. The most usual search methods are greedy hill climbing, either deterministic or stochastic, although other techniques have also been used. In this paper we pro...

The Weighted Set Covering Problem is a formal model for many practical optimization problems. In this problem the goal is to choose a subset of columns of minimal cost covering every row. Here, we present a novel application of the Artificial Bee Colony algorithm to solve the Weighted Set Covering Problem. The Artificial Bee Colony algorithm is a recent Swarm Metaheuristic technique based on th...

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm furth...

This paper presents a physics-inspired metaheuristic optimization algorithm, known as Electromagnetic Field Optimization (EFO). The proposed algorithm is inspired by the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, a possible solution is an electromagnetic particle made of electromagnets, and the number o...

Due to the law of reflection, a concave reflecting surface/mirror causes the incident light rays to converge and a convex surface/mirror causes the light rays to reflect away so that they all appear to be diverging. These converging and diverging behaviors cause that the curved mirrors show different image types depending on the distance between the object and the mirror. We model such optical ...

Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the results ...

Coordination of multi agent systems remains as a problem since there is no prominent method to completely solve this problem. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems with metaheuristic algorithms. The idea borrowed from swarm intelligence seems working much better than those implementations suggested...