Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Implementing soft computing techniques to solve economic dispatch problem in power systems
Expert Systems with Applications: An International Journal
A hybrid differential evolution method for dynamic economic dispatch with valve-point effects
Expert Systems with Applications: An International Journal
Fuzzy and simulated annealing based dynamic programming for the unit commitment problem
Expert Systems with Applications: An International Journal
An improved binary particle swarm optimization for unit commitment problem
Expert Systems with Applications: An International Journal
Evolutionary programming techniques for economic load dispatch
IEEE Transactions on Evolutionary Computation
A new evolutionary algorithm for non-linear economic dispatch
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Bio-inspired optimisation for economic load dispatch: a review
International Journal of Bio-Inspired Computation
Hi-index | 12.06 |
This paper proposes a novel Adaptive Real Coded Genetic Algorithm (ARCGA) to solve the nonconvex and nonsmooth economic dispatch (ED) problem considering valve loading effects and multiple fuel source options. Considering valve effects and multiple fuel options change ED into nonlinear, nonconvex and nonsmooth optimization problem with multiple minima. These characteristics challenge analytical and heuristic methods in finding optimal solution in reasonable time. The proposed ARCGA technique is composed of new genetic operators including arithmetic-average-bound crossover (AABX) and B-Spline wavelet mutation (BWM). Moreover, to enhance the computational efficiency of the suggested solution method, an adaptation process is also included in the ARCGA. To show the superiority of the ARCGA, it is compared with several most recently published methods proposed to solve the ED problem.