Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Operations Research
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Expert Systems with Applications: An International Journal
An application of swarm optimization to nonlinear programming
Computers & Mathematics with Applications
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Constrained optimization is a major real-world problem. Constrained optimization problems consist of an objective function subjected to both linear and nonlinear constraints. Here a constraint handling procedure based on the fitness priority-based ranking method (FPBRM) is proposed. It is embedded into a harmony search (HS) algorithm that allows it to satisfy constraints. The HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. Here, the original heuristic HS was improved by combining both improved and global-best methods along with the FPBRM. The resulting modified harmony search (MHS) was then compared with the original HS technique and other optimization methods for several test problems.