A new method called mutable smart bee (MSB) algorithm proposed for cooperative optimizing of the maximum power output (MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization (IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). According to obtained results, it can be concluded that Mutable Smart Bee (MSB) is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation 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. 1. Introduction The Atkinson cycle was designed by James Atkinson in 1882 [1]. This engine has two important advantages comparing to other engines; it is one of the most heat efficient as well as high expansion ratio cycles. Generally, four procedures called Intake, Compression, Power, and Exhaust take place in cycle per turn of crankshaft. In fact a classic Atkinson engine is a four-stroke engine and, in a same condition, it can reach a higher efficiency comparing to Otto cycle. Recently, researchers focused on analyzing and optimizing Atkinson cycle using different optimization techniques and intelligent controlling systems. Leff [2] determined the thermal efficiency of a reversible Atkinson cycle at maximum work output, Al-Sarkhi et al. [3] compared the performance characteristic curves of the Atkinson cycle to Miller and Brayton cycles using numerical examples and simulation techniques. Wang and Hou [4] studied the performance of Atkinson cycle in variable temperature heat reservoirs. Hou [5] investigated the effects of heat leak due to percentage of fuels energy, friction, and variable specific heats of working fluid. Here we proposed a new metaheuristic algorithm to analyze the performance of an air standard Atkinson cycle with heat transfer losses, friction, and variable specific heats of the working fluid. Metaheuristic algorithms are population-based methods working with a set of feasible solutions and trying to improve them gradually. These algorithms can be divided into two main parts: evolutionary algorithms (EAs) which attempt to simulate the phenomenon of natural evolution and swarm intelligence base
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