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Particle Swarm Optimization (PSO) with highly adaptive parameters and Chaotic Local Search (CLS) has been developed to obtain superior and robust convergence pattern. Depending on the objective function values of the current and best solutions in the present iteration, unique and innovative formulae are designed for two sets of PSO parameters, inertia weight & learning factors, to make them adaptive. To enrich the searching behavior and to avoid being trapped into local optimum, CLS is incorporated treating each individual particle as separate entity. Considering recent necessity and to prove the robustness and better effectiveness of the Chaotic Particle Swarm Optimization (CPSO) based algorithm, authors choose its application in power industry, as power flow has complex and non-linear characteristics. To the best of our knowledge, there is no published work on the CPSO to solve the power flow problems. PSO parameters are set to give better and reliable convergence characteristics for power flow under critical conditions like high R/X ratio and loadability limits. Conventional methods like Newton Raphson/Fast-decoupled load flow can not give multiple power flow solutions which are essential for voltage stability analysis. Proposed algorithm can overcome that limitation. The effectiveness and efficiency has been established showing results for standard and ill-conditioned systems.