Adaptive Split Artificial Ecosystem-Based Optimization to Solving Non-smooth Economic Dispatch

To overcome the main drawback of the original Artificial Ecosystems Optimization (AEO) algorithm, such as the premature convergence in solving specified and large optimization problems, a new adaptive mechanism search is introduced to create dynamic balance between exploration and exploitation during search process. In the proposed algorithm, the first modification introduced consists of dynamically adjusting the production operator to achieve the global search during the first trial, and then, during the next trials, the production operator adjusted dynamically to guide the other particles to perform local search. The second modification introduced within the standard algorithm is the adaptation of an interactive split technique to enhance the convergence behavior of the algorithm. In this paper the proposed Adaptive Split AEO (ASAEO) is applied to solve the non-smooth economic dispatch considering practical operation constraints, such as the valve point effect, the prohibited zones, the ramp rate limits and the total power losses. Also, and to relieve conflict on many results found in the recent literature using various metaheuristic methods and to demonstrate the particularity of the proposed power system planning strategy, a critical review is presented. In the literature, it is found that many comparative studies have been elaborated based on different databases, and these elaborated comparative studies may be misleading and difficult to judge the real contribution of many proposed optimization methods. Based on a statistical review, it is clearly confirmed that for the six test systems, two technical databases are available, and for the 13 generating units and 40 generating units, two databases are available in the literature. The particularity of the proposed variant named ASAEO has been validated on three standard test systems. The obtained results using the proposed technique compared to many recent methods are competitive for various test systems in terms of solution accuracy and convergence behavior.