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A revised particle swarm optimization based discrete Lagrange multipliers method for nonlinear programming problems

Mohammad Nezhad, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.cor.2010.11.007
  3. Abstract:
  4. In this paper, a new algorithm for solving constrained nonlinear programming problems is presented. The basis of our proposed algorithm is none other than the necessary and sufficient conditions that one deals within a discrete constrained local optimum in the context of the discrete Lagrange multipliers theory. We adopt a revised particle swarm optimization algorithm and extend it toward solving nonlinear programming problems with continuous decision variables. To measure the merits of our algorithm, we provide numerical experiments for several renowned benchmark problems and compare the outcome against the best results reported in the literature. The empirical assessments demonstrate that our algorithm is efficient and robust
  5. Keywords:
  6. Discrete Lagrange multipliers ; Particle swarm optimization ; Priority based feasibility strategy ; Bench-mark problems ; Constrained nonlinear programming problem ; Decision variables ; Discrete Lagrange multipliers ; Empirical assessment ; Lagrange multipliers method ; Local optima ; Nonlinear programming problem ; Numerical experiments ; Particle swarm ; Particle swarm optimization algorithm ; Priority-based ; Sufficient conditions ; Algorithms ; Nonlinear programming ; Particle swarm optimization (PSO) ; Lagrange multipliers
  7. Source: Computers and Operations Research ; Volume 38, Issue 8 , 2011 , Pages 1164-1174 ; 03050548 (ISSN)
  8. URL: http://www.sciencedirect.com/science/article/pii/S0305054810002777