Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
A method for interval 0-1 nonlinear programming problem using a genetic algorithm
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Solving a nonlinear non-convex trim loss problem with a genetic hybrid algorithm
Computers and Operations Research
Computers and Operations Research
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle Swarm Optimization applied to the design of water supply systems
Computers & Mathematics with Applications
Self-adaptive velocity particle swarm optimization for solving constrained optimization problems
Journal of Global Optimization
Optimization in water systems: a PSO approach
Proceedings of the 2008 Spring simulation multiconference
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Modeling fuzzy multi-period production planning and sourcing problem with credibility service levels
Journal of Computational and Applied Mathematics
Optimizing material procurement planning problem by two-stage fuzzy programming
Computers and Industrial Engineering
Bandwidth allocation with a particle swarm meta-heuristic for ethernet passive optical networks
Computer Communications
A mixed ant colony algorithm for function optimization
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A theoretical and empirical analysis of convergence related particle swarm optimization
WSEAS Transactions on Systems and Control
Quantum mechanics inspired Particle Swarm Optimisation for global optimisation
International Journal of Artificial Intelligence and Soft Computing
Engineering Applications of Artificial Intelligence
Computers and Industrial Engineering
Computers and Operations Research
An empirical analysis of convergence related particle swarm optimization
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Fuzzy two-stage material procurement planning problem
Journal of Intelligent Manufacturing
Particle swarm optimization for determining fuzzy measures from data
Information Sciences: an International Journal
Modified harmony search optimization for constrained design problems
Expert Systems with Applications: An International Journal
A direct solution approach to fuzzy mathematical programs with fuzzy decision variables
Expert Systems with Applications: An International Journal
Identification of surgical practice patterns using evolutionary cluster analysis
Mathematical and Computer Modelling: An International Journal
Firefly algorithm and pattern search hybridized for global optimization
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Particle swarm optimization (PSO) is an optimization technique based on population, which has similarities to other evolutionary algorithms. It is initialized with a population of random solutions and searches for optima by updating generations. Particle swarm optimization has become the hotspot of evolutionary computation because of its excellent performance and simple implementation. After introducing the basic principle of the PSO, a particle swarm optimization algorithm embedded with constraint fitness priority-based ranking method is proposed in this paper to solve nonlinear programming problem. By designing the fitness function and constraints-handling method, the proposed PSO can evolve with a dynamic neighborhood and varied inertia weighted value to find the global optimum. The results from this preliminary investigation are quite promising and show that this algorithm is reliable and applicable to almost all of the problems in multiple-dimensional, nonlinear and complex constrained programming. It is proved to be efficient and robust by testing some example and benchmarks of the constrained nonlinear programming problems.