Keywords

1 Introduction

Sustainable Computing is a standard that encompasses on a wide variety of policies, procedures, programs, and attitudes that execute the length and breadth and make use of information technologies. Sustainable Computing has been played a key role in industries, society, product and services such as sustainable energy, tools, process, and supply chains. Subsequently, we might be consider a sustainable computing from the perspective of green computing (maximize the energy efficiency , recyclability). Sustainable ICTs (Information and Communication Technology) and management of green computing will leads to significant benefit in energy efficiency. Due to globalization has caused most of the developed countries making an initiative to step rise in energy consumption. Moreover, sustainable IT green computing green strategies will have a significant impact and environment benefits on innovate, create value and build a competitive advantage. Further, the green IT will gain numerous advantages: (a) reduction of power and energy consumption, (b) improved resource utilization, (c) better operational efficiency, (d) reduce cost, fulfil the compliance and regulatory requirements and so on. Consequently, lot of research has been produced towards the development of energy models via Internet of Things (IoT), smart sensors, context-aware systems, pervasive, cloud computing systems, that try to optimize environmental sustainability. Furthermore, only limited research studies have investigated Computational Intelligence (CI) paradigms and their applications in building and predicting sustainable energy system. So, based on this context, this book have addressed the various CI prediction methodologies for the sustainable computing and analytics has been addressed in this chapter and this is also the focus of this edited book.

2 Intelligent Decision Support Computational Intelligence Paradigms

Computational Intelligence and its decision support methodologies have the power to attain knowledge about a specific task from given data. A system which is called computationally intelligent if it handles with structured/un-structured data during the decision making process of any application. This book mainly has designed the CI paradigms to solve complex real world sustainable computing problems. Moreover, the intelligent decision support CI paradigms which are extract the historical data in order to predict the future sustainable problems.

CI methodologies are of various divisions that are not limited to, Neuro computing, evolutionary computing, granular computing and artificial Immune system and so on. Moreover, CI mainly represents the integration of the following five methods from the soft computing perspective: Fuzzy Logic, Artificial Neural Networks , Evolutionary Computation, Learning Theory, and Probabilistic Methods. All these methods in fusion with one another helps the computer to solve a problem in the following way—(a) Fuzzy logic—understand natural language, (b) Artificial neural networks—to learn from experiential data by operating similar to the biological one, (c) Evolutionary computing—process of selection, (d) Learning theory—reasoning, and (e) Probabilistic methods—dealing with uncertainty imprecision.

Besides these main principles, there are some other approaches which include genetic algorithms , biologically inspired algorithms such as swarm intelligence and artificial immune systems. Recently there is an interest to fuse computational intelligence approaches with data mining, natural language processing, and artificial intelligence techniques. The detailed overview CI approaches on sustainable applications has been depicted in Fig. 1.

Fig. 1
figure 1

Integration of CI paradigms on sustainable computing applications

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In sustainable computing techniques, we always require to solve several of optimization problems like design, planning and control, which are really hard. Traditional mathematical optimization techniques are limited capacity and computationally difficult. Recent advances in computational intelligence paradigms have resulted in an in-creasing number of optimization techniques such as (a) Nature-inspired computational approaches: ant colony optimization, bee algorithm, firefly optimization, bacterial foraging optimization, artificial immune system and etc. (b) evolutionary computational approaches: Genetic algorithms , particle swarm optimization etc. (c) logical search algorithms: tabu search, harmony search, cross entropy method and so on for effectively solve these complex problems. Basically, the algorithms which are depends on the principle of natural biological evolution and/or collective behaviour of swarm have addressed a promising performance that has been reported many literature studies. Similarly, the earlier studies [1–3] have used computational intelligence techniques for parameter optimization (parameter tuning and parameter control) for computational sustainability issues. Subsequently, the earlier researchers [4, 5] have compared the recent CI paradigms for parameter optimization on various performance indicators.

3 CI Paradigms for Sustainable Applications

Computational Intelligence (CI) is the division of science and engineering where complex computational problems that are handled by modelling problems according to the natural and evolutionary intelligence, resulting in “intelligent systems” . These intelligent systems comprises numbers of popular intelligent algorithms; artificial neural networks , artificial immune systems, evolutionary computation, fuzzy system and swarm intelligence. Consequently, these intelligent algorithms are being a part of Artificial Intelligence (AI) . Alternatively, it is stated that Computational Intelligence (CI) is the successor of Artificial Intelligence (AI), where CI is analysis and study of adaptive mechanism to facilitate intelligent responses and is considered the sustainable computing and its applications. Recently, computational intelligence has emerged as a powerful methodology for revealing sustainable real-world challenging problems. The earlier study [6] have investigated the significance of CI, specifically neural networks in handling complexity and stochastic challenges with respect to smart grid system for addressing the new requirements of a sustainable global energy system.

Recently, there is an interest by researchers and practitioners have adopted CI paradigms for sustainable supply chain management. The previous studies [7, 8] have addressed the advances and applications of computational intelligence for sustainable supply chain planning in the context environmental sustainability (green design, green procurement, green production, green logistics, green packaging, green recycling). Subsequently, there are limited studies have focused on the supply chain management issue for supplier evaluation. Moreover, the studies have mostly focused on fuzzy multi-criteria decision making (MCDM) approaches such as Analytical Hierarchy Process, Analytical Network Process, DEMATEL, rough set theory for supplier evaluation. Further, these models have limited capacity (less robust) because computation in order quantity a specific supplier is not possible. To overcome such limitation hybrid fuzzy hybrid multi-criteria approaches has been investigated in the recent studies [9–11]. Still there are lots of research gaps needs to be addressed in the view meta-heuristic approaches for environmental sustainability. CI paradigms are used to find out the optimum solution of the problem. Moreover, subjective vagueness and imprecision can be effectively handled via CI approaches as a decision making tools for preventing the environmental challenges.

4 Importance of CI in Sustainable Computing Research

Intelligent decision support systems and sustainability will elaborate on CI paradigms deployment in many application areas of sustainability as well as the key challenges and opportunities that sustainability issues bring to CI research, education, and practice. This book has been focused upon the main themes at the intersection of CI and sustainability, but it will primarily concentrate on the larger contexts of sustainability, and on computing and sustainability, thereby setting the stage for extendable research needs to be carried out. The earlier study [12] has defined the sustainability as “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” Since there will be wide space for researchers large number sustainability challenges relating to energy, climate change, agriculture, transportation, disease, garbage and so on needs to be predict in advance in sustainable management. Similarity, CI paradigms and optimization algorithms have paved the way for effective design, and implementation of sustainable engineering applications. Typically, sustainable problems and challenges hard to handle with traditional approaches, to overcome such limitations, CI paradigms have take over a prominent role in planning, optimizing and forecasting sustainable systems. Typically, these methodologies would use of domain knowledge in order to obtain the required objectives. Hence, in the case that explicit domain knowledge is not presented, CI approaches could handle effectively with large raw numerical sensory data directly, process them, generate reliable and just-in-time responses, and have high fault tolerance.

The CI paradigms applied to sustainable computing and intelligent decision support systems have been paid more attention recently. Further, fusion of CI approaches such as neuro fuzzy approaches, artificial neural networks , evolutionary computation, swarm intelligence, rough sets can be incorporated to handle uncertainty and subjectivity in decision making process. However, the hybridization of CI techniques and optimization techniques has not been adequately investigated from the perspective of sustainable computing and analytics. Hence, there is an opportunity to address the emerging trends and advancement in sustainable application and process to by harnessing the power of computational intelligence. Future CI based applications would focus more on real-world problems that need a paradigm shift of paying attention to improving computational efficiency, understudying theoretical foundations and frameworks, and most essentially, supporting real decision-making in complex, uncertain application contexts. Taking ideas from CI which relate to sustainable computing and analytics to bringing into computational models would be helpful to researchers in this field to develop novel approaches and establish new research avenues to pursue.

5 Conclusion

In this chapter, the problem and challenges of sustainable computing in forecasting and decision making has been illustrated. The generic logic framework of the IDSS is highlighted, to outline the key functionalities of the CI on forecasting and the model repository as an open-ended subsystem including all relevant components for prediction. The research on development and application of CI paradigms and other meta-heuristic approaches can provide effective solutions for optimization problems, specifically, dealing with incomplete or inconsistent information and limited computational capability in handling sustainable problems. CI paradigms with relate to sustainable computing intelligent decision support and analytics bringing them into computational models can help researchers in this field to develop novel approaches and establish new research avenues to pursue.