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
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Journal of Global Optimization
Genetic algorithms using low-discrepancy sequences
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Population based metaheuristics are commonly used for global optimization problems. These techniques depend largely on the generation of initial population. A good initial population may not only result in a better fitness function value but may also help in faster convergence. Although these techniques have been popular since more than three decades very little research has been done on the initialization of the population. In this paper, we propose a modified Particle Swarm Optimization (PSO) called Improved Constraint Particle Swarm Optimization (ICPSO) algorithm for solving constrained optimization. The proposed ICPSO algorithm is initialized using quasi random Vander Corput sequence and differs from unconstrained PSO algorithm in the phase of updating the position vectors and sorting every generation solutions. The performance of ICPSO algorithm is validated on eighteen constrained benchmark problems. The numerical results show that the proposed algorithm is a quite promising for solving constraint optimization problems.