Most metaheuristics contain a randomness component, which is usually based on uniform randomization –i.e., the use of the Uniform probability distribution to make random choices. However, the Multi-start biased Randomization of classical Heuristics with Adaptive local search framework (MIRHA, Gonzalez-Martin et al., 2014a; Juan et al. ,2014a) proposes the use of biased (non-uniform) randomizati...

Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensiona...

We present two hybrid Metaheuristics, a hybrid Iterated Local Search and a hybrid Simulated Annealing, for solving real-world extensions of the Vehicle Routing Problem with Time Windows. Both hybrid Metaheuristics are based on the same neighborhood generating operators and local search procedures. The initial solutions are obtained by the Coefficient Weighted Distance Time Heuristics with autom...

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

In last few decades, the application of biological methods and systems to the study and design of engineering systems and modern technologies have fascinated many researchers. Numerous mathematical and meta-heuristic algorithms for solving optimization problems have been developed and widely used in both theoretical study and practical applications. To realize the application of such meta-heuri...

The traveling salesman problem (TSP) is a well-known NP-hard combinatorial optimization problem. The problem is easy to state, but hard to solve. Many real-world problems can be formulated as instances of the TSP, for example, computer wiring, vehicle routing, crystallography, robot control, drilling of printed circuit boards and chronological sequencing. In this paper, we present a modified hy...

Partricular features of overpassing local optima and providing near-optimal soultion in practical time has led researchers to apply metaheuristics in several engineering problems. Optimal design of diagrids as one of the most efficient structural systems in tall buildings has been concerned here. Jaya algorithm as a recent paramter-less optimization method is employed to solve the problem using...

The metaheuristics are approximation methods which deal with difficult optimization problems. The Work that we present in this paper has primarily as an objective the adaptation and the implementation of two advanced metaheuristics which are the Memetic Algorithms (MA) and the Electromagnetism Metaheuristic (EM) applied in the production systems of Hybrid Flow Shop (HFS) type for the problem of...

this paper investigates the problem of selecting and scheduling a set of projects among available projects. each project consists of several tasks and to perform each one some resource is required. the objective is to maximize total benefit. the paper constructs a mathematical formulation in form of mixed integer linear programming model. three effective metaheuristics in form of the imperialis...

This paper presents novel krill herd (KH) nature-inspired metaheuristics for solving portfolio optimization task. Krill herd algorithm mimics the herding behavior of krill individuals. The objective function for the krill movement is defined by the minimum distances of each individual krill from food and from higher density of the herd. Constrained portfolio optimization problem extends the cla...