Microsoft Excel Visual Basic for applications reference
Microsoft Excel Visual Basic for applications reference
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming
ACM Transactions on Mathematical Software (TOMS)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Using the particle swarm optimization technique to train a recurrent neural model
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization
Journal of Global Optimization
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Particle swarm approach for structural design optimization
Computers and Structures
Empirical investigation of the benefits of partial lamarckianism
Evolutionary Computation
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Expert Systems with Applications: An International Journal
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A particle swarm-BFGS algorithm for nonlinear programming problems
Computers and Operations Research
Hi-index | 12.05 |
This study deals with a new hybrid global-local optimization algorithm named PSOLVER that combines particle swarm optimization (PSO) and a spreadsheet ''Solver'' to solve continuous optimization problems. In the hybrid PSOLVER algorithm, PSO and Solver are used as the global and local optimizers, respectively. Thus, PSO and Solver work mutually by feeding each other in terms of initial and sub-initial solution points to produce fine initial solutions and avoid from local optima. A comparative study has been carried out to show the effectiveness of the PSOLVER over standard PSO algorithm. Then, six constrained and three engineering design problems have been solved and obtained results are compared with other heuristic and non-heuristic solution algorithms. Identified results demonstrate that, the hybrid PSOLVER algorithm requires less iterations and gives more effective results than other heuristic and non-heuristic solution algorithms.