Hybridizing bat algorithm with artificial bee colony for combined heat and power economic dispatch

This paper presents a new algorithm based on hybridizing Bat Algorithm (BA) and Artificial Bee Colony (ABC) with Chaotic based Self-Adaptive (CSA) search strategy (CSA-BA-ABC) to solve the large-scale, highly non-linear, non-convex, non-smooth, non-differential, non-continuous, multi-peak and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The proposed hybrid algorithm has better capability to escape from local optima with faster convergence rate than the standard BA and ABC. The proposed algorithm works based on the three mechanisms. The first one is a novel adaptive search mechanism, in which one of the three search phases (BA phase, directed onlooker bee phase and modified scout bee phase) is selected based on the aging level of the individual's best solution (pbest). In this regard, ABC's phases can assist BA phase to search based on deeper exploration /exploitation pattern as an alternative. In periodic intervals, the second mechanism called as CSA updates algorithm control parameters using chaotic system based on prevailing search efficiency in the swarm. Lastly, the third mechanism is enhancing the algorithm performance by incorporating individual's directional information, habitat selection and self-adaptive compensation. The effectiveness and robustness of the proposed algorithm are tested on a set of 23 benchmark functions and three CHPED problems. The obtained results by the suggested algorithm in terms of quality solution, computational performance and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better cost- effective solution.