Metaheuristic optimization algorithms for solving multiobjective economic dispatch problem

In this thesis, Artificial Cooperative Search (ACS) algorithm is incorporated with the Quadratic Approximation (QA) operator to solve the multiobjective Economic Emission Load Dispatch (EELD) problems with different generation units. ACS is a Swarm Intelligence (SI)-based metaheuristic algorithm, based on the interaction between prey and predator organisms in a habitat, is effective at global search; however, it does not perform so well at exploring promising regions. QA operator, on the other hand, is a non-derivative-based efficient local search method that finds the minimum of a quadratic hyperspace passing through three points in a D-dimensional space. Solving the EELD problems with the hybridized ACS-QA algorithm, as being proposed in the present thesis, leads to more accurate results with fewer function evaluations. Also, multiobjectivity of the problem is handled by transforming it into a single-objective problem by using the Weighted Sum Method (WSM). The efficiency of the proposed ACS-QA algorithm is tested in comparison to the algorithms existing in literature by implementing it on six different benchmark optimization problems. Afterwards, the proposed ACS-QA algorithm and the ACS algorithm are implemented on multiobjective EELD problems with different generation units. The results are compared with the solutions in literature utilizing different metaheuristic optimization algorithms. Both studies firmly showed that the ACS-QA algorithm is able to find more accurate results even though it uses fewer function evaluation calls.