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In this study, an extensive numerical analysis is carried out to investigate the effects of different quantum-based operators on the performance of continuous quantum-inspired evolutionary algorithms (QEAs). In this context, different variants of quantum-inspired evolutionary operators are adopted for numerical simulations. Furthermore, some novel chaos-enhanced QEAs are proposed and their performances are evaluated through the numerical comparative study. Based on evaluating the accuracy, robustness, convergence, scalability and sensitivity to initialisation of the rival methods, it is indicated that the algorithmic structure of QEAs is prone to being combined with chaotic maps. The results demonstrate that chaotically implemented QEAs can effectively explore/exploit the solution spaces of different landscapes and dimensionality, and finally, converge to acceptable regions within the solution domain. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid quantum-inspired evolutionary algorithm (QIEA) is proposed to automatically design regularised ensemble extreme learning machines (EELMs). Quantum evolutionary computing is a relatively recent spot-lighted concept which takes advantage from both the evolutionary and quantum computing laws. In general, QIEAs have been proven to be really powerful for optimising complex engineering tasks. The fascinating trait of observation operator in QIEA enables us to transform the quantum bits to both the binary and continuous spaces. Here, the authors present a mix continuous/binary version of QIEA, to find out whether it is suited for designing regularised EELMs. Indeed, the design process of EELM is conducted at two different levels, i.e. hyper and low levels. At the low level, some novel criteria are presented in the form of penalty functions to enable the optimiser searching for parsimonious, compact and accurate regularised extreme learning machines, as individual components of the ensemble. At the hyper-level, the non-negative least square error optimisation technique is utilised to deterministically find the most eligible components for designing the ensemble. Through extensive numerical experiments, the authors demonstrate that the proposed method is really efficient for the automated design of EELM identifiers. [ABSTRACT FROM AUTHOR]

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain. [ABSTRACT FROM AUTHOR]

In this study, a comprehensive empirical test is conducted to analyse the effects of two well-known chaotic maps, namely sinusoidal and logistic maps, on the efficacy of double Pareto crossover, Laplace crossover and simulated binary crossover operators for the global optimization of continuous problems. To do so, 13 well-known numerical benchmark problems in three distinctive dimensions, namely 50D, 100D and 200D, are considered and the genetic algorithm (GA) with simple version and chaos-enhanced versions of the mentioned crossover operators are utilized for optimizing these functions. Furthermore, a time complexity analysis is conducted to find out the impact of hybridizing the chaos and the evolutionary operators on the computational complexity of GA. The results of the experimental analysis provide us with fruitful information regarding the scalability, computational complexity and exploration/exploitation capability of the considered rival optimization algorithms, as well as, demonstrate the efficacy of chaos-evolutionary computing for numerical continuous optimizations. [ABSTRACT FROM AUTHOR]

In this investigation, the authors intend to find out whether chaos-embedded heuristic walks with nature-based controllers are capable of increasing the performance of the standard random walks of evolutionary algorithms. Indeed, the research motivation emanates in the pursuit of addressing an increasing interest in using chaos sequences for modifying the exploitation capability of evolutionary optimisers. Here, the authors propose a nature-based controller to dynamically balance the exploration/exploitation capability of the evolutionary optimiser. To test the validity of the proposed method, a number of well-known Gaussian distribution-based evolutionary operators are considered, and their random parameters are changed with discrete-time chaotic sequences. Besides, to obtain authentic and reliable results regarding the accuracy and robustness of the rival techniques, the authors went through the existing literature and extracted 23 scalable, multimodal, constraint and unconstraint numerical benchmark problems and one engineering problem, i.e. optimal control of shape memory alloy actuators. The convergence rate and computational time of the resulting self-controlled chaos-based evolutionary approaches are analysed to find out whether they are capable of stabilising the chromosomes within a logical time. The results of the numerical experiments indicate that the self-controlled chaos-enhanced heuristic walks significantly increase the global searching capability of the evolutionary optimisers and have an aptitude to show a robust performance. [ABSTRACT FROM PUBLISHER]

In this paper, the authors intend to attest the applicability of computational intelligence for tackling a demanding real-life engineering problem, that is analysing the amounts of exhaust gas temperature () and the engine-out hydrocarbon emission () during the coldstart operation of an automotive engine with respect to the variation of engine speed (), spark timing (Δ) and air/fuel ratio. It has been proven that the coldstart phenomenon is highly transient, nonlinear and uncertain and therefore has absorbed an increasing attention of the researchers of automotive society. In this paper, we prove that a proper integration of intelligent methods can result in a very efficient tool suited for identifying the characteristics of coldstart phenomenon. To do so, a recent spotlighted natural-inspired optimiser called mutable smart bee algorithm is utilised to evolve a Gaussian generalised regression neural network such that the resulted framework can conduct a fast, robust and accurate identification. The outcomes of the conducted experiments endorse on the applicability of intelligent techniques for engine coldstart identification, and also, pave the way for future investigations on intelligent-based analysis of demanding automotive problems. [ABSTRACT FROM AUTHOR]

In this investigation, a comparative experimental study is conducted to obtain an efficient hybrid intelligent framework for analysing and optimising the operating parameters of the laser solid freeform fabrication (LSFF) process. Here, the experimental studies are conducted in two different stages. In the first stage, different concepts of machine learning systems are taken into account to find a simple yet accurate intelligent model for identifying the LSFF process. To do so, multi-layered neural network with different types of analytical, gradient-based and heuristic learning strategies, i.e. extreme learning machine, back-propagation and steepest descend gradient-based learning and Nelder–Mead simplex heuristic, respectively, are adopted and applied to the LSFF process. In the second stage, different types of swarm- and evolutionary-based metaheuristics, i.e. differential evolutionary algorithm, particle swarm optimisation, the great salmon run, firefly algorithm, bee algorithm, are used to simultaneously find the optimal values of melt-pool depth and clad height during the LSFF process. The statistical results of the simulation indicate that the conducted experiments can result in a fast, robust and accurate hybrid intelligent system which can easily cope with the nonlinearities and uncertainties of the resulting optimisation problem. [ABSTRACT FROM AUTHOR]