Ameliorated Grey Wolf Optimization for Economic Load Dispatch Problem

Owing to no free lunch theorem, meta-heuristic search algorithms, behaviour and performance are different for solving specific class of optimization problems than another. Further, search algorithms are conceptualized of exploration and exploitation aspects. Ameliorated grey wolf optimization algorithm is proposed to solve economic power load dispatch problem that synergizes between exploration and exploitation aspects. Ameliorated grey wolf optimization procedure coordinates the behaviour of grey wolves, random exploratory search, local random search and opposition learning heuristics. Grey wolf optimization algorithm is considered to perform global search and random exploratory search applied to perform a search in the neighbourhood of already visited search locations. To maintain good solutions and diversity among solutions, opposition based learning has been implemented. The practical aspects i.e. value-point loading effect, avoidance of prohibited operating zones and ramp rate limits, generation limits of generators and satisfaction of power demand constraint are undertaken to solve economic power load dispatch problem. Heuristic search is exploited to handle equality constraints. The proposed algorithm is validated on standard benchmark functions and medium to large electric power systems. Results reveal that proposed technique is a potential method to solve economic load dispatch problems as it competes with recent algorithms undertaken for comparison.