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Abstract-The spark and compression ignition principles of petrol and diesel internal combustion engines (ICEs) have not advanced for a century. These do not lead to complete combustion and hence result in high exhaust emission and low energy efficiency. This paper presents a comprehensive survey on the attempts and developments of greener ignition and combustion systems for ICEs and points out that homogeneous charge microwave ignition (

Abstract-With the proliferating development of heuristic methods, it has become challenging to choose the most suitable ones for an application at hand. This paper evaluates the performance of these algorithms available in Matlab, as it is problem dependent and parameter sensitive. Further, the paper attempts to address the challenge that there exists no satisfied benchmarks to evaluation all the algorithms at the same standard. The paper tests five heuristic algorithms in Matlab, the Nelder-Mead simplex search, the Genetic Algorithm, the Genetic Algorithm with elitism, Simulated Annealing and Particle Swarm Optimization, with four widely adopted benchmark problems. The Genetic Algorithm has an overall best performance at optimality and accuracy, while PSO has fast convergence speed when facing unimodal problem.

Abstract-Most internal combustion engines are built on compression or spark ignition, which is far from optimal and the problem of which is more than optimization. This paper first improves a genetic algorithm (GA) for such an application, aiming at the potential invention of a homogeneous charge microwave ignition (HCMI) engine. For an HCMI system, search for optimal emitters under the intrinsic constraints of resonant frequencies forms a coupled constraint optimization problem and poses an intractable challenge to the GA and virtual prototyping for the invention. A predefined GA (PGA) is then developed to handle appropriate frequency ranges for this problem so as to allow the parameters of the emitter, as well as its structure, to be optimized in an evolutionary process. The heuristic search is compared with the deterministic NM simplex and the nondeterministic conventional GA. Results show that while the NM and GA heuristics find an insufficient mode, the PGA often finds the global maximum, with a higher convergence rate and independent of the algorithm's initial settings. When the complexity of the problem increases with the number of variables, the PGA also delivers a robust performance while the NM and the GA yield divergent results. This application confirms the viability and power of evolutionary heuristics in inventing novel real-world solutions if properly adapted.