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This paper presents a coevolutionary algorithm named cooperative coevolutionary invasive weed optimization (CCIWO) and investigates its performance for global optimization of functions with numerous local optima and also Nash equilibrium (NE) search for games. Ability of CCIWO for function optimization is tested through a set of common benchmarks of stochastic optimization, and reported results are compared with two other coevolutionary algorithms. In advance, a three-bus transmission-constrained electricity market model is studied, and CCIWO is employed to find NE for this complex system. Experimental results show efficiency of the proposed method to have more accurate solutions.