Optimal design of classic Atkinson engine with dynamic specific heat using adaptive neuro-fuzzy inference system and mutable smart bee algorithm

Abstract

In this article, an improved version of Artificial Bee Colony (ABC) algorithm is developed to optimize a multi-modal thermodynamic power system with dynamic specific heat. Since original Karaboga's ABC for constraint problems does not consider the initial population to be feasible, a modified method called “Mutable Smart Bee Algorithm” (MSBA) is used which utilizes mutable heuristic agents. These mutable agents are also capable to maintain their historical memory for the location and quality of food sources. These features have been found as strong elements for mining data in constraint areas. In additions, our implementations reveal that MSBA is faster than Karaboga's method. To elaborate on authenticity of MSBA, several state-of-the-art techniques are used as rival methods to optimize well-known benchmark problems. Then, two main steps are made to optimize Atkinson engine. Firstly, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed to identify the dynamic behavior of specific heat. Then, MSBA is hired to design the optimum features of the engine. It is observed that the proposed method is capable to successfully handle the real-life engineering problem as well as the numerical benchmark problems.