Economic Load Dispatch for Piecewise Quadratic Cost Function using Hybrid Self-adaptive Differential Evolution with Augmented Lagrange Multiplier Method

This paper presents an efficient method for solving economic load dispatch (ELD) problems using a hybrid self- adaptive differential evolution with augmented Lagrange multiplier method (SADE_ALM). Treated as additional control variables, two strategic parameters called the mutation factor (F) and the crossover constant (CR) are dynamically self-adaptive throughout the evolutionary process. Since tuning of the parameters is a tedious task due to complex relationship among parameters, the optimal parameter settings may never be found, and possibly leads to a local optimal solution. An augmented lagrange multiplier method (ALM) is applied to handle equality/inequality constraints. To demonstrate the effectiveness of the proposed algorithm, two ELD problems considering: (1) multiple fuels, and (2) multiple fuels with valve-point effects, are tested and compared with other methods e.g. differential evolution (DE) based methods, modified particle swarm optimization (MPSO), improved genetic algorithm with multiplier updating (IGA_MU) etc. The results show that the proposed SADE_ALM is very effective and provides promising capability for solving the economic load dispatch problem with piecewise quadratic cost function.