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In recent decades, development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers. This refers to the essence of real-life engineering systems and complex natural mechanisms which are generally multi-modal, non-convex and multi-criterion. Until now, several deterministic and stochastic methods have been proposed to cope with such complex systems. Advanced soft computational methods such as evolutionary games (cooperative and non-cooperative), Pareto-based techniques, fuzzy evolutionary methods, cooperative bio-inspired algorithms and neuro-evolutionary systems have effectively come to the aid of researchers to build up efficient paradigms with application to vector optimization. The paper aims to discuss this issue.

A novel hybrid algorithm called synchronous self-learning Pareto strategy (SSLPS) is presented for the sake of vector optimization. The method is the ensemble of evolutionary algorithms (EA), swarm intelligence (SI), adaptive version of self-organizing map (CSOM) and a data shuffling mechanism. EA are powerful numerical optimization algorithms capable of finding a global extreme point over a wide exploration domain. SI techniques (the swarm of bees in our case) can improve both intensification and robustness of exploration. CSOM network is an unsupervised learning methodology which learns the characteristics of non-dominated solutions and, thus, enhances the quality of the Pareto front.

To prove the effectiveness of the proposed method, the authors engage a set of well-known benchmark functions and some well-known rival optimization methods. Additionally, SSLPS is employed for optimal design of shape memory alloy actuator as a nonlinear multi-modal real-world engineering problem. The experiments show the acceptable potential of SSLPS for handling both numerical and engineering multi-objective problems.

To the author’s best knowledge, the proposed algorithm is among the rare multi-objective methods which fosters the use of automated unsupervised learning for increasing the intensity of Pareto front (while preserving the diversity). Also, the research evaluates the power of hybridization of SI and EA for efficient search.

The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper penalty function, regularization laws are embedded into the structure of common least square solutions to increase the numerical stability, sparsity, accuracy and robustness of regression weights. Several regularization techniques have been proposed so far which have their own advantages and disadvantages. Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques. However, the proposed numerical and deterministic approaches need certain knowledge of mathematical programming, and also do not guarantee the global optimality of the obtained solution. In this research, the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine (ELM).

To implement the required tools for comparative numerical study, three steps are taken. The considered algorithms contain both classical and swarm and evolutionary approaches. For the classical regularization techniques, Lasso regularization, Tikhonov regularization, cascade Lasso-Tikhonov regularization, and elastic net are considered. For swarm and evolutionary-based regularization, an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered, and its algorithmic structure is modified so that it can efficiently perform the regularized learning. Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme. To test the efficacy of the proposed constraint evolutionary-based regularization technique, a wide range of regression problems are used. Besides, the proposed framework is applied to a real-life identification problem, i.e. identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine, for further assurance on the performance of the proposed scheme.

Through extensive numerical study, it is observed that the proposed scheme can be easily used for regularized machine learning. It is indicated that by defining a proper objective function and considering an appropriate penalty function, near global optimum values of regressors can be easily obtained. The results attest the high potentials of swarm and evolutionary techniques for fast, accurate and robust regularized machine learning.

The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine (OP-ELM). The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system, and also increases the degree of the automation of OP-ELM. Besides, by using different types of metaheuristics, it is demonstrated that the proposed methodology is a general flexible scheme, and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.

The purpose of this paper is to probe the potentials of computational intelligence (CI) and bio-inspired computational tools for designing a hybrid framework which can simultaneously design an identifier to capture the underlying knowledge regarding a given plug-in hybrid electric vehicle’s (PHEVs) fuel cost and optimize its fuel consumption rate. Besides, the current investigation aims at elaborating the effectiveness of Pareto-based multiobjective programming for coping with the difficulties associated with such a tedious automotive engineering problem.

The hybrid intelligent tool is implemented in two different levels. The hyper-level algorithm is a Pareto-based memetic algorithm, known as the chaos-enhanced Lamarckian immune algorithm (CLIA), with three different objective functions. As a hyper-level supervisor, CLIA tries to design a fast and accurate identifier which, at the same time, can handle the effects of uncertainty as well as use this identifier to find the optimum design parameters of PHEV for improving the fuel economy.

Based on the conducted numerical simulations, a set of interesting points are inferred. First, it is observed that CI techniques provide us with a comprehensive tool capable of simultaneous identification/optimization of the PHEV operating features. It is concluded that considering fuzzy polynomial programming enables us to not only design a proper identifier but also helps us capturing the undesired effects of uncertainty and measurement noises associated with the collected database.

To the best knowledge of the authors, this is the first attempt at implementing a comprehensive hybrid intelligent tool which can use a set of experimental data representing the behavior of PHEVs as the input and yields the optimized values of PHEV design parameters as the output.

The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy (SMA) actuators, as one of the most applicable types of actuators in engineering and industry. The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators.

To address the requirements of designing a pragmatic identification system, the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion. First, the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model. At the next step, they adopt a computationally efficient metaheuristic algorithm to elicit the optimum operating parameters of the gray box identifier.

Applying the proposed hybrid identifier framework allows the authors to find out its advantages in modeling the behavior of SMA actuator. Through different experiments, the authors conclude that the proposed identifier can be used for identification of highly nonlinear dynamic behavior of SMA actuators. Furthermore, by extending the conclusions and expounding the obtained results, one can easily infer that such a hybrid method may be conveniently applied to model other engineering phenomena that possess dynamic nonlinear reactions. Based on the exerted experiments and implementing the method, the authors come to the conclusion that integrating the power of metaheuristic exploration/exploitation with gray box identifier results a predictive paradigm that much more computationally efficient as compared with black box identifiers such as neural networks. Additionally, the derived gray box method has a higher degree of preference over the black box identifiers, as it allows a manipulated expert to extract the knowledge of the system at hand.

The originality of the research paper is twofold. From the practical (engineering) point of view, the authors built a prototype biased-spring SMA actuator and carried out several experiments to ascertain and validate the parameters of the model. From the computational point of view, the authors seek for designing a novel identifier that overcomes the main flaws associated with the performance of black-box identifiers that are the lack of a mean for extracting the governing knowledge of the system at hand, and high computational expense pertinent to the structure of black-box identifiers.

The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks (NNs), called multiple-valued logic neural networks (MVLNN), for predicting the behavior of phenomenological systems with highly nonlinear dynamics. MVLNNs are constructed based on the integration of a number of neurons working based on the principle of multiple-valued logics. MVLNNs possess some particular features, namely complex-valued weights, input, and outputs coded by kth roots of unity, and a continuous activation as a mean for transferring numbers from complex spaces to trigonometric spaces, which distinguish them from most of the existing NNs.

The presented study can be categorized into three sections. At the first part, the authors attempt at providing the mathematical formulations required for the implementation of ARX-based MVLNN (AMVLNN). In this context, it is indicated that how the concept of ARX can be used to revise the structure of MVLNN for online applications. Besides, the stepwise formulation for the simulation of Chua’s oscillatory map and multiple-valued logic-based BP are given. Through an analysis, some interesting characteristics of the Chua’s map, including a number of possible attractors of the state and sequences generated as a function of time, are given.

Based on a throughout simulation as well as a comprehensive numerical comparative study, some important features of AMVLNN are demonstrated. The simulation results indicate that AMVLNN can be employed as a tool for the online identification of highly nonlinear dynamic systems. Furthermore, the results show the compatibility of the Chua’s oscillatory system with BP for an effective tuning of the synaptic weights. The results also unveil the potentials of AMVLNN as a fast, robust, and efficient control-oriented model at the heart of NMPC control schemes.

This study presents two innovative propositions. First, the structure of MVLNN is modified based on the concept of ARX system identification programming to suit the base structure for coping with chaotic and highly nonlinear systems. Second, the authors share the findings about the learning characteristics of MVLNNs. Through an exhaustive comparative study and considering different rival methodologies, a novel and efficient double-stage learning strategy is proposed which remarkably improves the performance of MVLNNs. Finally, the authors describe the outline of a novel formulation which prepares the proposed AMVLNN for applications in NMPC controllers for dynamic systems.

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.

In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.

Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC.

The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.

The main occupational safety measure in factories is monitoring workers exposed to various types of contaminations. The main environmental concern of governments about copper industries is emission of dust, metals, metal compounds and volatile organic compounds in air. The purpose of this paper is to evaluate the immune system status of workers in a copper concentration factory in Iran by placing the emphasis on oxidative stress biomarkers.

A comparative cross-sectional study was performed on 40 workers of the copper concentration factory and 40 unexposed individuals. White blood cell count, plasma interleukin 2 and 4, oxidative burst of neutrophils, oxidative damages of DNA and RNA, lipid and protein, total antioxidant capacity of plasma, and antioxidant enzymes activities were measured.

A significant decrease in the white blood cell count and interleukin 2 and an increase in the interleukin 4 were observed in the workers and these changes represented the possibility of inflammation and weakening of the immune system. The elevation of oxidative damages, total antioxidant capacity and the activity of antioxidant enzymes are indicative of the change in oxidative stress status.

The oxidative stress induction and immune system changes might be useful biomarkers in screening and surveillance for occupational hazard. More studies are needed to find out the type and the concentration of pollutants and to evaluate the protective effects of natural antioxidants.

This study aims to better understand the Islamic consumption incentives because the spectacular flourishing of the halal market in different places around the world has grown the interest in understanding and deciphering the mechanisms behind its development.

Through an exploratory study of some Tunisia-based Islamic groups’ purchasing behavior, this paper investigates factors leading to the purchasing of halal goods (Islamic consumption).

Findings show that the Islamic consumer is more of an Islamist than simply a Muslim. In addition, findings show that halal consumption is not merely related to religious affiliations but also the product of numerous cultural, social and psychological factors.

In addition to Islamism and Islamic activism, this paper puts in evidence the role of some post-structural factors such as identity, nostalgia and hedonism in relation to the buying intention of halal products and services.

Institutional changes, in a historical context, through simultaneous evolutionary and metamorphic processes either deform or reform long-enduring institutions. The chapter delves into the Persian history from the early days of the reign of Nāṣer al-Dīn Shāh-e Qājār in 1848 to the recent years and traces Persian institutions' historical transformations, which culminated to the Persian women entrepreneurship. Thus, the chapter first sets the historical context in each period and then sheds light on the pivotal issues of each period's women. The undergirding base of the discussions is the assumption of the change in institutions as natural metamorphosis in the animate. Finally, the discussions contribute to the conceptualization of the Institutional Triangulation and in the case of Persia, a cultural-driven triangulation, which has paved the way to the formation of a stupendously hegemonic patriarchal and masculine sociopolitical economy in Persia, that has historically affected women's institutionalization, subjugation, subordination, marginalization, socialization, emancipation, and most recently Islamization phases.

Innovative health-promoting approaches of the era have verified phytoceutics as one of the prime therapeutic tools to alleviate numerous health-related ailments. The purpose of this paper is to probe the nutraceutic potential of ginger flowers and leaves against hyperglycemia.

The aqueous extracts of ginger flowers and leaves were observed on Sprague Dawley rats for 8 weeks. Two parallel studies were carried out based on dietary regimes: control and hyperglycemic diets. At the end of the experimental modus, the overnight fed rats were killed to determine the concentration of glucose and insulin in serum. The insulin resistance and insulin secretions were also calculated by formulae by considering fasting glucose and fasting insulin concentrations. Furthermore, the feed and drink intakes, body weight gain and hematological analysis were also carried out.

In streptozotocin-induced hyperglycemic rats, the ginger flowers extract depicted 5.62% reduction; however, ginger leaves extract reduced the glucose concentration up to 7.11% (p = 0.001). Similarly, ginger flowers extract uplifted the insulin concentration up to 3.07%, while, by ginger leaves extract, the insulin value increased to 4.11% (p = 0.002). For the insulin resistance, the ginger flower showed 5.32% decrease; however, the insulin resistance was reduced to 6.48% by ginger leaves (p = 0.014). Moreover, the insulin secretion increased to 18.9% by flower extract and 21.8% by ginger leave extract (p = 0.001). The feed intake and body weight gain increased momentously by the addition of ginger flowers and leaves; however, the drink intake and hematological analysis remained non-significant by the addition of ginger parts.

Conclusively, it was revealed that leaves have more hypoglycemic potential as compared to flowers.