A Novel Brown-bear Optimization Algorithm for Solving Economic Dispatch Problem

For optimal operation of a modern power system, an efficient optimizer is needed. There are many conventional and intelligent system-based optimization tools available. Conventional approaches suffer from many drawbacks such as the requirement of derivatives of objective function and constraints, close initial guess, etc. Getting optimal solutions using intelligent system-based methods is challenging too. Stable and reliable operation of the power system depends a lot on the efficient solutions to such problems. In this chapter, a novel optimizer named as Brown-Bear Optimization Algorithm (BOA) based on the mode of communication between brown-bears featuring pedal scent marking and sniffing behaviors is introduced to solve the Economic Dispatch Problem (EDP) which is an important problem of optimal operation of power system. Pedal scent marking behavior of bears, which is a fundamental mode of communication between them, is characterized by different features such as maintaining a characteristic gait while walking, careful stepping on the pedal marks and twisting of feet on depressions made on ground Sniffing behavior found in them further strengthens their communication. The mathematical model featuring the aforementioned behaviors of bears is developed to form BOA. The algorithm maintains a balance between exploration and exploitation and is free from algorithm-specific parameters. To examine the performance of BOA, it is applied to solve several benchmark tests functions of different complexities. Comparative assessment is carried out to establish its efficacy. The proposed algorithm is found to be performing better than many existing optimization algorithms in obtaining an optimal solutions for benchmark test functions. To identify its statistical significance, Wilcoxon’s signed rank test is conducted. The test reveals that BOA produces more significant results in comparison to other state-of-art algorithms Further, BOA is utilized to solve EDP while minimizing the total cost involved subjected to practical constraints. The results obtained from BOA are found to be minimum when compared to the results of state-of-art optimization algorithms reported in the literature.