This paper introduces a hybrid metaheuristic, the Variable Neighborhood Particle Swarm Optimization (VNPSO), consisting of a combination of the Variable Neighborhood Search (VNS) and Particle Swarm Optimization(PSO). The proposed VNPSO method is used for solving the multi-objective Flexible Job-shop Scheduling Problems (FJSP). The details of implementation for the multi-objective FJSP and the c...

In this paper we discuss the application of a metaheuristic approach based on the Scatter Search to deal with robust optimization of the planning problem in the deploying of the Dense Wavelength Division Multiplexing (DWDM) technology on an existing optical fiber network taking into account, in addition to the forecasted demands, the uncertainty in the survivability requirements. Keyword: Netwo...

The Berth Allocation Problem aims at assigning and scheduling incoming vessels to berthing positions along the quay of a container terminal. This problem is a well-known optimization problem within maritime shipping. For solving it, we propose two POPMUSIC (Partial Optimization Metaheuristic Under Special Intensification Conditions) approaches that incorporate an existing mathematical programmi...

The Ant Colony Optimization (ACO) metaheuristic [1] is a constructive population-based approach based on the social behavior of ants. As it is acknowledged as a powerful method to solve academic and industrial combinatorial optimization problems, a considerable amount of research is dedicated to improving its performance. Among the proposed solutions, we find the use of parallel computing to re...

Economic Load Dispatch (ELD) problem in power systems has been solved by various optimization methods in the recent years, for efficient and reliable power production. This paper introduces a solution to ELD problem using a new metaheuristic nature-inspired algorithm called Firefly Algorithm (FFA). The proposed approach has been applied to various test systems. The results proved the efficiency...

Economic Load Dispatch problem (ELD) is one of the most important optimization problems in power system operation and planning. This paper introduces a solution to ELD problem using a new metaheuristic natureinspired algorithm called Cuckoo Search Algorithm (CSA). The proposed approach has been applied to various systems. This algorithm is based on the obligate brood parasitic behavior of some ...

Ant Colony Optimization (ACO) algorithm is a novel metaheuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation o...

The problem of detecting suitable parameters for metaheuristic optimization algorithms is well known long since. As these nondeterministic methods, e.g. evolution strategies (ES) [1], are highly adaptible to a specific application, detecting good parameter settings is vital for their success. Performance differences of orders of magnitude (in time and/or quality) are often achieved by means of ...

A new approach for the problem of optimal task scheduling in flexible manufacturing systems is proposed in this work, as a combination of metaheuristic optimization techniques with the supervisory control theory of discrete-event systems. A specific encoding, the word-shuffling encoding, which avoids the generation of a large number of infeasible sequences, is employed. A metaheuristic method b...

A combinatorial optimization metaheuristic called Sensitive Ant Model (SAM) based on the Ant Colony System (ACS) technique is proposed. ACS agents cooperate indirectly using pheromone trails. SAM improves and extends the ACS approach by enhancing each agent of the model with properties that induce heterogeneity. Agents are endowed with different pheromone sensitivity levels. Highly-sensitive ag...