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A simplified binary artificial fish swarm algorithm for uncapacitated facility ...

Uncapacitated facility location problem (UFLP) is a combinatorial optimization problem, which has many applications. The artificial fish swarm algorithm has recently emerged in continuous optimization problem. In this paper, we present a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving the UFLP. In S-bAFSA, trial points are created by using crossover and mutation. In order to im...

Modified constrained differential evolution for solving nonlinear global optimi...

Nonlinear optimization problems introduce the possibility of multiple local optima. The task of global optimization is to find a point where the objective function obtains its most extreme value while satisfying the constraints. Some methods try to make the solution feasible by using penalty function methods, but the performance is not always satisfactory since the selection of the penalty parameters for the pr...

A simplified binary artificial fish swarm algorithm for 0–1 quadratic knapsack ...

Documento submetido para revisão pelos pares. A publicar em "Journal of computational and applied mathematics". ISSN 0377-0427. Available online 8 October 2013 (http://www.sciencedirect.com/science/article/pii/S0377042713005074) ; This paper proposes a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving 0–1 knapsack problems. This is a combinatorial optimization problem, whic...

Improved binary artificial fish swarm algorithm for the 0–1 multidimensional kn...

Documento submetido para revisão pelo pares a publicar em Swarm and evolutionary computation. ISSN 2210-6502. Versão "In Press, Corrected Proof" disponível em http://www.sciencedirect.com/science/article/pii/S2210650213000552?np=y ; The 0–1 multidimensional knapsack problem (MKP) arises in many fields of optimization and is NP-hard. Several exact as well as heuristic methods exist. Recently, an artificial fish...

Solving 0–1 quadratic knapsack problems with a population-based artificial fish...

A modified differential evolution based solution technique for economic dispatc...

Economic dispatch (ED) plays one of the major roles in power generation systems. The objective of economic dispatch problem is to find the optimal combination of power dispatches from different power generating units in a given time period to minimize the total generation cost while satisfying the specified constraints. Due to valve-point loading effects the objective function becomes nondifferentiable and has ...

Modified differential evolution based on global competitive ranking for enginee...

Engineering design optimization problems are formulated as large-scale mathematical programming problems with nonlinear objective function and constraints. Global optimization finds a solution while satisfying the constraints. Differential evolution is a population-based heuristic approach that is shown to be very efficient to solve global optimization problems with simple bounds. In this paper, we propose a mo...

Glocal competitive ranking for constraints handling with modified differential ...

Constrained nonlinear programming problems involving a nonlinear objective function with inequality and/or equality constraints introduce the possibility of multiple local optima. The task of global optimization is to find a solution where the objective function obtains its most extreme value while satisfying the constraints. Depending on the nature of the involved functions many solution methods have been prop...

Nonlinear continuous global optimization by modified differential evolution

The task of global optimization is to find a point where the objective function obtains its most extreme value. Differential evolution (DE) is a population-based heuristic approach that creates new candidate solutions by combining several points of the same population. The algorithm has three parameters: amplification factor of the differential variation, crossover control parameter and population size. It is r...