New multi-objective stochastic search technique for economic load dispatch

A new multi-objective stochastic search technique (MOSST) for the multi-objective economic dispatch problem in power systems is presented. It is a highly constrained problem with both equality and inequality constraints. The MOSST heuristic has been designed as a combination of real coded genetic algorithms (GA) and simulated annealing (SA). It incorporates a genetic crossover operator BLX-alpha and a problem specific mutation operator with a local search heuristic to provide a better search capability. Extensive simulations are carried out on standard test systems, considering various aspects, and the results are compared with other methods. These results indicate that the new MOSST heuristic converges rapidly to improved solutions. MOSST is a truly multi-objective technique, as it provides the values of various parameters for optimising different objectives, as well as the best compromise between them, all in a single run. Perturbation analysis shows that the solutions obtained by MOSST are truly pareto-optimal, i.e. no objective can be further improved without degrading the others.