Title: Optimising maximum power output and minimum entropy generation of Atkinson cycle using mutable smart bees algorithm
Authors: Mofid Gorji; Ahmad Mozaffari; Sina Mohammadrezaei Noudeh
Addresses: Department of Mechanical Engineering, Noshirvani University of Technology, P.O. Box. 484, Babol, Iran. ' Department of Mechanical Engineering, Noshirvani University of Technology, P.O. Box. 484, Babol, Iran. ' Department of Mechanical Engineering, Noshirvani University of Technology, Babol, Iran
Abstract: The purpose of this article is optimising maximum power output (MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multi-objective constraint thermodynamic problem by a new improved artificial bee colony algorithm which utilises 'mutable smart bee' (MSB) instead of conventional bees. The results have been checked with some of the most common optimising algorithms like Karaboga's original artificial bee colony, bees algorithm (BA), improved particle swarm optimisation (IPSO), Lukasik firefly algorithm (LFFA) and self-adaptive penalty function genetic algorithm (SAPF-GA). Mutable smart bee (MSB) is able to maintain its historical memories for the location and quality of food sources and also a little chance of mutation during the searching process is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.
Keywords: mutable smart bee algorithm; artificial bee colony; Atkinson cycle; maximum power density; minimum entropy generation; MEG; metaheuristics; swarm intelligence; evolutionary algorithms; multi-objective constraint thermodynamics; data mining.
International Journal of Computational Science and Engineering, 2012 Vol.7 No.2, pp.108 - 120
Received: 03 Aug 2011
Accepted: 26 Oct 2011
Published online: 22 Sep 2014 *