Mass optimization on shape and sizing with multiple natural frequency constraints are highly nonlinear dynamic optimization problems. Multiple natural frequency constraints normally cause difficult dynamic sensitivity analysis and, in addition, two different types of design variables, nodal coordinates and crosssectional areas, often lead to divergence. Thus, the choice of the appropriated meth...

Simplified Dolphin Echolocation (SDE) optimization is an improved version of the Dolphin Echolocation optimization. The dolphin echolocation (DE) is a recently proposed metaheuristic algorithm, which was imitated dolphin’s hunting process. The global or near global optimum solution modeled as dolphin’s bait, dolphins send sound in different directions to discover the best bait among their searc...

The Traveling Salesman Problem (TSP) is a classical NPhard combinatorial problem that has been intensively studied through several decades. A large amount of literature is dedicated to this problem. TSP can be directly applied to some real life problems, and to be generalized to some other set of important combinatorial problems. Nowadays, a large collection of TSP instances can be found such a...

Multi-dimensional Visual Tracking (MVT) problems include visual tracking tasks where the system state is defined by a high number of variables corresponding to multiple model components and/or multiple targets. A MVT problem can be modeled as a dynamic optimization problem. In this context, we propose an algorithm which hybridizes Particle Filters (PF) and the Scatter Search (SS) metaheuristic,...

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...

We suggest a new heuristic for solving unconstrained continuous optimization problems. It is based on a generalized version of the variable neighborhood search metaheuristic. Different neighborhoods and distributions, induced from different metrics are ranked and used to get random points in the shaking step. We also propose VNS for solving constrained optimization problems. The constraints are...

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between firefli...

Ant Colony Optimization (ACO) is a class of metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This paper is composed of three parts. The first one frames ACO in current trends of research on metaheuristics for combinatorial optimization. The secon...

Computational finance has become one of the emerging application fields of metaheuristic algorithms. In particular, these optimization methods are quickly becoming the solving approach alternative when dealing with realistic versions of financial problems, such as the popular portfolio optimization problem (POP). This paper reviews the scientific literature on the use of metaheuristics for solv...