Abd Allah A. Mousa

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In this paper, we present a hybrid approach combining two optimization techniques for solving economic emission load dispatch (EELD) optimization problem. The proposed approach integrates the merits of both genetic algorithm (GA) and local search (LS), where it employs the concept of co-evolution and repair algorithm for handling nonlinear constraints,(More)
This paper presents an efficient genetic algorithm for solving multiobjective transportation problem, assignment, and transshipment Problems. The proposed approach integrates the merits of both genetic algorithm (GA) and local search (LS) scheme. The algorithm maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the(More)
This paper presents a novel multiobjective design optimization problem. The multiobjective design problem can be cast in theses terms: maximize the inductance and minimize the volume of a coreless solenoid. In order to obtain a set of equivalent optimal solutions, a hybrid ant colony optimization approach has been implemented. The proposed approach differs(More)
Copyright © 2013 Abd Allah A. Galal et al. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT In this paper, a new optimization system based genetic algorithm is presented. Our approach(More)
— Artificial neural networks are massively paralleled distributed computation and fast convergence and can be considered as an efficient method to solve real-time optimization problems. In this paper, we propose reference point based neural network algorithm for solving fuzzy multiobjective optimization problems MOOP. The target is to identify the(More)
In this paper, a new hybrid optimization system is presented. Our approach integrates the merits of both ant colony optimization and steady state genetic algorithm and it has two characteristic features. Firstly, Since there is instabilities in the global market and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem(More)
This paper presents an enhanced Particle Swarm Optimization (PSO) algorithm applied to the reactive power compensation (RPC) problem. It is based on the combination of Genetic Algorithm (GA) and PSO. Our approach integrates the merits of both genetic algorithms (GAs) and particle swarm optimization (PSO) and it has two characteristic features. Firstly, the(More)
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