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Learning theory
(Elsevier, 2024) Pour Haji Kazem, Ali Asghar
This chapter addresses fundamental aspects of learning theory, its ethical implications in model development and deployment, its integration with decision-making processes, and future directions. The introduction highlights learning theory's role in bridging data and informed choices, while ethical considerations underscore the need to address bias, transparency, and societal impact. The interplay between learning theory and decision-making is explored, enabling systems to navigate uncertainty. Looking ahead, the evolution of learning theory is expected to encompass explainable AI, neuroscience influences, ethical considerations, and integration with emerging technologies, promising transformative advancements across various domains. © 2024 Elsevier Inc. All rights reserved.
Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning
(Elsevier, 2024) Allahviranloo, Tofigh; Pedrycz, Witold; Seyyedabbasi, Amir
Decision Making Models: A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science. The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real-life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences. Researchers, professors, software engineers, and graduate students working in the fields of applied mathematics, software engineering, and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems. Other areas of note include optimization problems and artificial intelligence practices, as well as how to analyze IoT solutions with applications and develop decision-making mechanisms realized under uncertainty. © 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
Computations with words
(Elsevier, 2024) Allahviranloo, Tofigh
In fact, computing with words is a method in which the objects are words, and the computations are propositions extracted from ordinary conversation. For example, small, large, far, and heavy, not very likely, the price of gas in Iran is low and increasing a lot. Computing with words is inspired by the remarkable ability of humans to perform various types of physical and mental activities without any measurement or calculation. Familiar examples of these activities are parking a car, driving in heavy traffic, riding a bicycle, understanding speech, etc. © 2024 Elsevier Inc. All rights reserved.
Neuro-fuzzy systems
(Elsevier, 2024) Zeinalnezhad, Masoomeh; Allahviranloo, Tofigh; Pedrycz, Witold
The neuro-fuzzy system has gotten the great attention of researchers in numerous scientific areas due to its practical reasoning and learning capabilities. This chapter aims to help researchers to get a brief overview of the fuzzy sets and the related operations. The following introduces various classifications of neuro-fuzzy systems and discusses two groups of neuro-fuzzy models, including Mamdani and Takagi-Sugeno-Kang (TSK). The final section presents the most recent applications of neuro-fuzzy systems in various fields, such as environment and health, energy, and industry. This chapter will be helpful for researchers studying the development of neuro-fuzzy systems and their applications. © 2024 Elsevier Inc. All rights reserved.
Artificial intelligent algorithms, motivation, and terminology
(Elsevier, 2024) Pour Haji Kazem, Ali Asghar
In the era of rapid technological advancements, few fields have garnered as much attention and excitement as artificial intelligence. The fusion of human-like intelligence with machines has not only transformed industries but also reshaped the very fabric of our lives. This article takes you on a captivating journey through the realms of artificial intelligent algorithms, delves into the motivational aspects driving this innovation, and familiarizes you with the essential terminology that underpins this fascinating domain. © 2024 Elsevier Inc. All rights reserved.
Single and multi-objective metaheuristic algorithms and their applications in software maintenance
(Elsevier, 2024) Arasteh, Bahman; Sadegi, Razieh; Aghaei, Babak; Ghanbarzadeh, Reza
The comprehension of software structure plays a significant role in efficiently maintaining software. Clustering software modules has been regarded as a successful method for extracting understandable structural models from source code, among other reverse engineering techniques. However, the problem of achieving the optimal model for clustering is considered NP-complete. The primary objective of Software Module Clustering (SMC) is minimizing inter-cluster connections, maximizing intra-cluster connections, and improving clustering quality. Different optimization algorithms (Seyyedabbasi, 2023) have been used to sort out the optimization problems. The majority of proposed methods to address SMC problem have shown some drawbacks, such as a lower rate of success, stability, and quality of modularization. This chapter reviews and compares seven heuristic algorithms that can be employed to solve software module clustering, namely PSO, GA, PSO-GA, COA, GWO, SCSO and OOA, in terms of achieving optimal clustering of software modules. Through experiments conducted on 10 real-world standard applications, the findings demonstrate that OOA, GWO, and SCSO perform better than the other methods in handling SMC. Notably, when the initial population of these methods is generated by the use of the logistic chaos technique instead of the random technique, these algorithms perform much better compared to others. The average quality of the modularity of the clusters created by OOA, GWO, and SCSO for the selected benchmark set is 3.937, 3.120, 3.107, respectively. The findings present an exploration of heuristic algorithms for optimal SMC; therefore, the positive impact of chaos theory is highlighted. OOA, GWO, SCSO, and COA demonstrate promising results, indicating their potential for practical application in the field of software maintenance and comprehension. By addressing the drawbacks of the evaluated methods, this chapter contributes to the advancement of software clustering techniques and facilitates the creation of more maintainable and efficient software systems.
Soft modeling
(Elsevier, 2024) Babakordi, Fatemeh; Taghi-Nezhad, Nemat Allah; Allahviranloo, Tofigh
In this chapter of the book, we examine five practical and real-world models in the fuzzy environment. First, we discuss the fuzzy solution for the cancer tumor model under generalized Hukuhara-Caputo partial derivability using the fuzzy integral transformation. Then, the fuzzy classification system, which is used as a decision support tool for biologists in identifying dendritic cells, was investigated in the fuzzy environment. In the following, the quantitative model of economic production with decline under Marxist economics is expressed in the method of social political economy. The study of the model in a fuzzy environment is inevitable to fit the model to the real problem as much as possible. Another model that has been studied and is very efficient in the fuzzy environment is the market equilibrium value model. Finally, we explain the inventory model with the learning effect and the business credit strategy in the fuzzy environment for the buyer.
Discretized optimization algorithms for finding the bug-prone locations of a program source code
(Elsevier, 2024) Arasteh, Bahman; Sefati, Seyed Salar; Shami, Shiva; Abdollahian, Mehrdad
The number of discovered bugs determines the efficacy of software test data. Software mutation testing is an important issue in software engineering since it is used to evaluate the effectiveness of test techniques. Syntactical changes are made to the program source code to generate buggy versions, which are then run alongside the original programs using test data. However, one of the key disadvantages of mutation testing is its high processing cost, which presents a difficult dilemma in the field of software engineering. The major goal of this study is to investigate the performance of different heuristic algorithms associated with mutation testing. According to the 80%–20 rule, 80% of a program's bugs are detected in only 20% of its bug-prone code. Different heuristic algorithms have been proposed to find out the bug-prone and sensitive locations of a program source code. Next, mutants are only put into the identified bug-prone instructions and data. This technique guarantees that mutation operators are only injected into code portions that are prone to bugs. Experimental evaluation on typical benchmark programs shows the effectiveness of different heuristic algorithms in reducing the number of generated mutants. A decrease in the number of created mutants reduces the total cost of mutation testing. Another feature of the heuristic-based mutation testing technique is its independence from platform and testing tool. Experimental findings show that using the heuristic strategy in different testing tools such as Mujava, Muclipse, Jester, and Jumble results in a considerable reduction of mutations created during testing. © 2024 Elsevier Inc. All rights reserved.
Uncertain optimization (with a special focus on data envelopment analysis)
(Elsevier, 2024) Amirteimoori, Alireza; Allahviranloo, Tofigh; Shahriari, Mohammadreza
Uncertain optimization refers to contexts where there is uncertainty in models and data. It potentially has various applications in different domains such as portfolio selection, inventory management, pollution reduction, sustainable development, resource allocation and reallocation, and performance analysis. In real life, decisions often need to be made under unknown scenarios. In our terminology, uncertainty refers to the variability of data and optimization refers to the analysis and solution of a problem that involves optimizing an objective, given a set of constraints. This chapter deals with uncertain optimization problems with a special emphasis on data envelopment analysis (DEA). Since many books discuss fuzzy optimization problems, only the stochastic type of uncertainty in data and models is considered here.
Uncertainty theory
(Elsevier, 2024) Allahviranloo, Tofigh
The main goal of this chapter is to introduce the sources of uncertainty and define different types of uncertainty. To define uncertainty, we must first define the measurable quantities and then the measurable space. This is followed by the uncertain variable and finally the uncertain space. Meanwhile, the operators for these sources are also defined.
Chaos theory and chaotic systems
(Elsevier Original, 2024) Allahviranloo, Tofigh
This chapter introduces chaos theory and chaotic systems as they arise and operate as a principle in real life. The chapter discusses the facts in general. © 2024 Elsevier Inc. All rights reserved
A comprehensive survey: Swarm-based algorithms
(Elsevier, 2024) Seyyedabbasi, Amir
The swarm-based algorithm is a type of algorithm inspired by natural phenomena. Swarm-based algorithms have been successfully used to solve many Np-hard optimization problems. Swarm-based algorithms have been found to be particularly effective for solving complex optimization problems. In addition to their ability to handle complex and nonlinear search spaces, these algorithms are also constrained by computational complexity and premature convergence. It should be noted, however, that swarm-based algorithms are not suitable for all optimization problems. Several effective strategies have been proposed in order to overcome this limitation, including the hybridization of other algorithms. In addition to its computational complexity, it may not always be the optimal solution to each problem, due to premature convergence and computational complexity. © 2024 Elsevier Inc. All rights reserved.
A comprehensive survey: Nature-inspired algorithms
(Elsevier, 2024) Seyyedabbasi, Amir
Recently, metaheuristic algorithms have become increasingly important. The purpose of this chapter is to provide readers with an overview of metaheuristic algorithms. This chapter provides an overview of the key elements of these metaheuristic algorithms including physics-based, evolution-based, and swarm-based algorithms and their evolutionary operators and functionalities. There have also been surveys examining these algorithms, but a comprehensive comparison and contrast study is lacking in current survey papers. As this chapter will introduce each algorithm individually, detailed introductions will be provided for each algorithm. There has been a great deal of effort devoted to this chapter to compare the metaheuristic algorithms that have been proposed in the last decade and, from among them, the most popular ones have been chosen for discussion in this chapter. Each algorithm has been evaluated according to the performance of well-known benchmark functions to determine its performance. As a result of this comparative study, we are aiming to provide a broader view of nature-inspired algorithms and meaningful insights into their design and implementation. The remaining of this section is: Nature-inspired algorithms. Physics-based algorithms. Evolution-based algorithms. Swarm-based algorithms. Multiobjective algorithms. Unconstrained/constrained nonlinear optimization. © 2024 Elsevier Inc. All rights reserved.
A comprehensive survey: Evolutionary-based algorithms
(Elsevier, 2024) Seyyedabbasi, Amir
Evolutionary algorithms (EAs) are optimization algorithms based on natural selection and evolution. The operators selected, reproduce, crossover, and mutation are used to evolve a population of candidate solutions iteratively. A variety of complex optimization problems in diverse domains have been successfully solved using EAs. An overview of genetic algorithms (GAs), differential evolution (DE), and genetic programming (GP) is presented in this chapter. It emphasizes the capabilities of EAs, including the ability to explore large problem spaces, handle nonlinear and multimodal search spaces, and accommodate a wide range of objectives and constraints. Although these algorithms are capable of handling complex and nonlinear search spaces, they also face challenges such as computational complexity and premature convergence. In order to enhance their performance, researchers are focusing on hybridization, parameter tuning, and parallelization. These algorithms will remain important tools in optimization and machine learning as computational resources increase, with promising future prospects in a wide range of fields. © 2024 Elsevier Inc. All rights reserved.
Approximate reasoning
(Elsevier, 2024) Pedrycz, Witold; Allahviranloo, Tofigh
The chapter is focused on the fundamentals of approximate reasoning, their key formal properties, design, and an overall development process. We also show how the mechanisms of approximate reasoning support the construction of rule-based models, which are one of the omnipresent constructs in fuzzy modeling. © 2024 Elsevier Inc. All rights reserved.
Applied optimization problems
(Elsevier, 2024) Rahmaniperchkolaei, Bijan; Taeeb, Zohreh; Shahriari, Mohammadreza; Lotfi, Farhad Hosseinzadeh; Saati, Saber
Applied Optimization, a pivotal discipline in various fields, involves the strategic utilization of mathematical techniques to enhance decision-making, resource allocation, and problem-solving across diverse domains. This abstract highlights the significance of Applied Optimization by delving into its multifaceted importance. In an era marked by intricate challenges and resource constraints, Applied Optimization emerges as a potent tool to streamline complex processes. Its ability to identify optimal solutions within the constraints of real-world scenarios empowers organizations and individuals to maximize efficiency, minimize costs, and achieve desired outcomes. From industrial engineering and supply chain management to finance, healthcare, and beyond, Applied Optimization offers tailored solutions that resonate with specific challenges. This abstract underlines that the true essence of Applied Optimization lies not just in mathematical elegance but in its tangible impact. By optimizing processes, decisions, and strategies, Applied Optimization accelerates progress, propelling businesses toward competitive advantage and societal sectors toward sustainable development. This chapter, dedicated to exploring the realm of Applied Optimization, delves into its methodologies, models, and practical applications. It underscores the interconnectedness of optimization with the modern world, emphasizing that its impact extends far beyond theoretical constructs. Through real-world case studies and in-depth analyses, readers gain insights into how Applied Optimization reshapes industries and transforms societies. In conclusion, Applied Optimization stands as an indispensable tool that transcends disciplines and permeates every facet of our lives. This abstract encapsulates its importance as an enabler of efficient resource utilization, strategic decision-making, and the attainment of optimal outcomes in an ever-evolving world. © 2024 Elsevier Inc. All rights reserved.
Fuzzy sets
(Elsevier, 2024) Allahviranloo, Tofigh
As we all know, there are so many papers and chapters about fuzzy sets, but the book that this chapter is one of its chapters must bring up this topic again. This chapter discusses and presents different types of fuzzy sets and their properties. It also contains the algebraic operators between the sets. © 2024 Elsevier Inc. All rights reserved.

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