Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
The Theory of Discrete Lagrange Multipliers for Nonlinear Discrete Optimization
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Simulated annealing with asymptotic convergence for nonlinear constrained optimization
Journal of Global Optimization
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Expert Systems with Applications: An International Journal
Solving nonlinearly constrained global optimization problem via an auxiliary function method
Journal of Computational and Applied Mathematics
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
An application of swarm optimization to nonlinear programming
Computers & Mathematics with Applications
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
An intelligent augmentation of particle swarm optimization with multiple adaptive methods
Information Sciences: an International Journal
Parallel-machine scheduling to minimize tardiness penalty and power cost
Computers and Industrial Engineering
A particle swarm-BFGS algorithm for nonlinear programming problems
Computers and Operations Research
Introducing polynomial fuzzy time series
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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.