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Search Results: 1 - 10 of 9496 matches for " Bilevel Multiobjective Optimization "
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Solution Concepts and New Optimality Conditions in Bilevel Multiobjective Programming  [PDF]
Francisque Fouodji Dedzo, Laure Pauline Fotso, Calice Olivier Pieume
Applied Mathematics (AM) , 2012, DOI: 10.4236/am.2012.330196
Abstract: In this paper, new sufficient optimality theorems for a solution of a differentiable bilevel multiobjective optimization problem (BMOP) are established. We start with a discussion on solution concepts in bilevel multiobjective programming; a theorem giving necessary and sufficient conditions for a decision vector to be called a solution of the BMOP and a proposition giving the relations between four types of solutions of a BMOP are presented and proved. Then, under the pseudoconvexity assumptions on the upper and lower level objective functions and the quasiconvexity assumptions on the constraints functions, we establish and prove two new sufficient optimality theorems for a solution of a general BMOP with coupled upper level constraints. Two corollary of these theorems, in the case where the upper and lower level objectives and constraints functions are convex are presented.
Hybrid Swarm Algorithm for Multiobjective Optimal Power Flow Problem  [PDF]
K. Rajalashmi, S. U. Prabha
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.711304
Abstract: Optimal power flow problem plays a major role in the operation and planning of power systems. It assists in acquiring the optimized solution for the optimal power flow problem. It consists of several objective functions and constraints. This paper solves the multiobjective optimal power flow problem using a new hybrid technique by combining the particle swarm optimization and ant colony optimization. This hybrid method overcomes the drawback in local search such as stagnation and premature convergence and also enhances the global search with chemical communication signal. The best results are extracted using fuzzy approach from the hybrid algorithm solution. These methods have been examined with the power flow objectives such as cost, loss and voltage stability index by individuals and multiobjective functions. The proposed algorithms applied to IEEE 30 and IEEE 118-bus test system and the results are analyzed and validated. The proposed algorithm results record the best compromised solution with minimum execution time compared with the particle swarm optimization.
Solving Bilevel Linear Multiobjective Programming Problems  [PDF]
Calice Olivier Pieume, Patrice Marcotte, Laure Pauline Fotso, Patrick Siarry
American Journal of Operations Research (AJOR) , 2011, DOI: 10.4236/ajor.2011.14024
Abstract: This study addresses bilevel linear multi-objective problem issues i.e the special case of bilevel linear programming problems where each decision maker has several objective functions conflicting with each other. We introduce an artificial multi-objective linear programming problem of which resolution can permit to generate the whole feasible set of the upper level decisions. Based on this result and depending if the leader can evaluate or not his preferences for his different objective functions, two approaches for obtaining Pareto- optimal solutions are presented.
Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem  [PDF]
Abd Allah A. Mousa, Mohamed A. El-Shorbagy
Applied Mathematics (AM) , 2012, DOI: 10.4236/am.2012.330184
Abstract: 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 algorithm is initialized by a set of a random particle which traveling through the search space, during this travel an evolution of these particles is performed by a hybrid PSO with GA to get approximate no dominated solution. Secondly, to improve the solution quality, dynamic version of pattern search technique is implemented as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective RPC.
The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm  [PDF]
K. Liagkouras, K. Metaxiotis
Journal of Software Engineering and Applications (JSEA) , 2013, DOI: 10.4236/jsea.2013.67B005

The paper addresses the constrained mean-semivariance portfolio optimization problem with the support of a novel multi-objective evolutionary algorithm (n-MOEA). The use of semivariance as the risk quantification measure and the real world constraints imposed to the model make the problem difficult to be solved with exact methods. Thanks to the exploratory mechanism, n-MOEA concentrates the search effort where is needed more and provides a well formed efficient frontier with the solutions spread across the whole frontier. We also provide evidence for the robustness of the produced non-dominated solutions by carrying out, out-of-sample testing during both bull and bear market conditions on FTSE-100.

On Metaheuristic Optimization Motivated by the Immune System  [PDF]
Mohammed Fathy Elettreby, Elsayd Ahmed, Houari Boumedien Khenous
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.52032

In this paper, we modify the general-purpose heuristic method called extremal optimization. We compare our results with the results of Boettcher and Percus [1]. Then, some multiobjective optimization problems are solved by using methods motivated by the immune system.

Finding the Efficient Frontier for a Mixed Integer Portfolio Choice Problem Using a Multiobjective Algorithm  [PDF]
iBusiness (IB) , 2009, DOI: 10.4236/ib.2009.12013
Abstract: We propose a computational procedure to find the efficient frontier for the standard Markowitz mean-variance model with discrete variables. The integer constraints limit on the one hand the portfolio to contain a predetermined number of assets and, on the other hand, the proportion of the portfolio held in a given asset. We adapt the multiobjective algorithm NSGA for solving the problem. The algorithm ranks the solutions of each generation in layers based on Pareto non-domination. We have applied the procedure in sixty assets of ATHEX. We have also compared the algorithm with a single genetic algorithm. The computational results indicate that the procedure is promising for this class of problems.
A General Class of Convexification Transformation for the Noninferior Frontier of a Multiobjective Program  [PDF]
Tao Li, Yanjun Wang, Zhian Liang
American Journal of Operations Research (AJOR) , 2013, DOI: 10.4236/ajor.2013.33036
Abstract: A general class of convexification transformations is proposed to convexify the noninferior frontier of a multiobjective program. We prove that under certain assumptions the noninferior frontier could be convexified completely or partly after transformation and then weighting method can be applied to identify the noninferior solutions. Numerical experiments are given to vindicate our results.

Local Search-Inspired Rough Sets for Improving Multiobjective Evolutionary Algorithm  [PDF]
Ahmed A. EL-Sawy, Mohamed A. Hussein, El-Sayed Mohamed Zaki, Abd Allah A. Mousa
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.513192

In this paper we present a new optimization algorithm, and the proposed algorithm operates in two phases. In the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm is based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept e-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems.

Análisis multiobjetivo a un sistema energético
Bastidas,Marlon; Jaramillo,Patricia; Chejne,Farid; Galván,Blas;
Revista Facultad de Ingeniería Universidad de Antioquia , 2010,
Abstract: an optimization methodology for complex energy system introducing external factors based on the newly developed pareto-based multiobjective evolutionary algorithms (moea) used for solving a real-world power systems multiobjective nonlinear optimization problem is presented. the thermoeconomic, technology and environment objectives are included in this methodology and weights are assigned to each objective for evaluating the fitness and average sum. the complex energy system is integrated by a combined-cycle power plant (subsystem i) and a gasifier (subsystem ii).
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