Ensemble mutable smart bee algorithm and a robust neural identifier for optimal design of a large scale power system

2015

Meccanica

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In this paper, an attempt is made to develop a bio-inspired soft sensor as a real-time identifier for online estimations of the engine-out hydrocarbon emission and exhaust gas temperature over the coldstart operation of an automotive engine. To date, a number of studies have been done to design soft models as offline identifiers for capturing the knowledge of a set of collected coldstart databases. The results indicated that bio-inspired computation can be of great use for the identification of coldstart phenomenon. It also can be a good alternative to classic approaches. However, bio-inspired soft sensors have not been used as online estimators for monitoring the behavior of coldstart phenomenon. This is while it is desirable to have a flexible, inexpensive online identifier which can be employed to detect the realtime behavior of the engine over coldstart periods.Here, an exhaustive numerical study is conducted to demonstrate the potentials of bio-inspired approaches for online measuring of engine-out hydrocarbon emission and exhaust gas temperature during the coldstart operation. To do so, a set of well-known bioinspired optimizers are combined with a time-delayed artificial neural network to design an evolvable soft sensor. The results clearly demonstrate the high potential of bio-inspired computation for handling the difficulties associated with the real-time identification of engine-out hydrocarbon emission and exhaust gas temperature.

Section: Mutable Smart Bee Algorithmmentioning

confidence: 99%

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

2015

Meccanica

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“…They proved that the method is best suit for coping with difficulties and constraints of the optimisation problem. Recently, an ensemble version of MSBA has been proposed for optimising a large-scale power system under uncertainty (Mozaffari, Azimi, & Gorji-Bandpy, 2013). Based on a comparative study, the authenticity of ensemble MSBA for optimising large-scale optimisation problems has been proved.…”

Section: The Proposed Intelligent Analysing Tool 41 Mutable Smart Bementioning

confidence: 99%

Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission

Journal of Experimental & Theoretical Artificial Intelligen

2014

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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 (T exh ) and the engine-out hydrocarbon emission (HC raw ) during the coldstart operation of an automotive engine with respect to the variation of engine speed (4 e ), spark timing (D) 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.

“…The authors' experiences demonstrate that the AGS mutation operator is very effective for optimizing multimodal and difficult problems [27,28]. The AGS operator is mathematically expressed as follows:…”

Section: Mutation Operatormentioning

confidence: 99%

“…In fact, the time complexity analysis demonstrates which types of chaotic maps are more computationally efficient, and also lets us understand whether they are more parsimonious in comparison to the standard variants of GAs. In a previous work by the authors, it has been observed that the time complexity is a proper metric for evaluating the complexity of the methods[27].The mathematical formulations required for calculating the time complexity are given belowis the code execution time for 200000 evaluations of the programming operators, namely %, , +, -, ^, /, exp, and ln, for a simple 2 dimensional vector, 1  is the required time for 200000 function evaluations and 2  is the mean of 30 execution times.…”

mentioning

confidence: 99%

An empirical investigation into the effects of chaos on different types of evolutionary crossover operators for efficient global search in complicated landscapes

Emami

International Journal of Computer Mathematics

et al. 2014

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In this study, a comprehensive empirical test is conducted to analyze the effects of two well-known chaotic maps, namely sinusoidal and logistic maps, on the efficacy of double Pareto crossover (DPX), Laplace crossover (LX) and simulated binary crossover (SBX) 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.