Field Of Computational Intelligence Research Articles

Preface

The 2024 International Conference on Computational Intelligence and Communication System (CCICS2024), held in Wuhan, China from June 26-28, 2024, is a testament to the ever-evolving nature of fields of computational intelligence and communication technology, has attracted a diverse and brilliant array of researchers, engineers, and scholars from around the globe. CCICS2024 attracted more than 80 participants from around the world, which is a vibrant and engaging event, with a diverse program that included an opening ceremony, keynote speeches, oral presentations, poster sessions, Q&A discussions, and an ending ceremony. The keynote speakers, renowned experts worldwide in their respective fields, provided insightful overviews of the latest developments and trends in domains related to computational intelligence and communication technology. The oral presentations and poster sessions were equally enriching. Researchers and practitioners shared their latest findings, discussed challenges and opportunities, and explored potential collaborations. The lively discussions and interactions among the participants created a vibrant intellectual atmosphere that fostered innovation and knowledge sharing. The submissions received for this conference are vivid evidence of the creativity and innovation that flourishes in related areas. The manuscripts cover a wide range of topics, including computing for emerging technology, signal processing, sensor and detection technology, futuristic communication and network technologies, etc. The diversity and depth of these contributions reflect the vibrant and interdisciplinary nature of relevant industries. All papers submitted to the conference have been reviewed using a double-blind peer-review process. Each paper was tested for similarity with already published content using the Turnitin platform. All sources of similarities were checked and only original content was accepted. This volume of proceedings is beneficial for readers from academia and industry to understand and tackle the challenges efficiently and adopt appropriate solutions in the above-mentioned topics. Building on the success of this event, we hope CCICS 2024 will be one of the most spectacular events in those areas. We would like to thank the reviewers and the members of the international scientific committees. We also extend our sincere thanks to Prof. Joarder Kamruzzaman, Prof. Yangjun Chen, Prof. Oscar Eduardo Ruiz Salguero, Prof. Jie Yang, Prof. JingTao Yao and Dr. Iacovos Ioannou for their wonderful keynote talks. We are grateful to everyone who has contributed to the realization of CCICS 2024. We also look forward to the next conferences in this series, which will continue to build upon the foundation laid by this one. Organizing Committee of CCICS 2024 Prof. Yuriy S. Shmaliy (Conference Chair) IEEE Fellow, AAIA Fellow, University of Guanajuato, Mexico List of Conference Committee is available in this Pdf.

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions.

Chaos and multi-layer attractors in asymmetric neural networks coupled with discrete fractional memristor

This article introduces a novel model of asymmetric neural networks combined with fractional difference memristors, which has both theoretical and practical implications in the rapidly evolving field of computational intelligence. The proposed model includes two types of fractional difference memristor elements: one with hyperbolic tangent memductance and the other with periodic memductance and memristor state described by sine functions. The authenticity of the constructed memristor is confirmed through fingerprint verification. The research extensively investigates the dynamics of a coupled neural network model, analyzing its stability at equilibrium states, studying bifurcation diagrams, and calculating the largest Lyapunov exponents. The results suggest that when incorporating sine memristors, the model demonstrates coexisting state variables depending on the initial conditions, revealing the emergence of multi-layer attractors. The article further demonstrates how the memristor state shifts through numerical simulations with varying memductance values. Notably, the study emphasizes the crucial role of memductance (synaptic weight) in determining the complex dynamical characteristics of neural network systems. To support the analytical results and demonstrate the chaotic response of state variables, the article includes appropriate numerical simulations. These simulations effectively validate the presented findings and provide concrete evidence of the system’s chaotic behavior.

A particle swarm optimization algorithm with novelty search for combustion systems with ultra-low emissions and minimum fuel consumption

The particle swarm optimization algorithm is primarily inspired by the natural behaviour of swarms and achieves important results in different applications. However, it is not exempt from stagnation in local optima and has a tendency to prematurely converge to them. Novelty Search is a concept that appeared recently in different fields of computational intelligence. It aims at exploring non-visited areas of the search space through solutions that bring novelty to already discovered solutions. The novelty of this work can be divided into two steps: on one side, this article proposes a variant of the particle swarm optimization algorithm which uses Novelty Search concepts to improve the algorithm’s performance. Our proposal is first checked and compared using the CEC 2005 benchmark suite and then, we apply it to solve a real-world optimization problem: the design of a combustion system targeting the reduction of pollutant emissions and fuel consumption. The combustion chamber design phase usually is a complex and time-consuming process even with advanced supercomputers, since it depends on several input variables which are highly non-linear and with crossed interaction. Then, the second contribution of this work is to develop a methodology that couples a computational fluid dynamics (CFD) simulation tool with the new optimization algorithm for minimizing the specific fuel consumption of a compression-ignited engine, while constraining the NOx and soot emissions. A 3D-CFD model of the combustion system was built to predict and analyse the performance of the combustion system and hence, select the parameters with a higher impact on the system. The method reduces the computational time and includes tools for the automatic preparation of the input parameters and geometry of the system. The input parameters correspond to geometrical variables that control the bowl shape, the number of holes in the injector, the injection pressure, the swirl number and the exhaust gas recirculation rate. Results show how the simulation tool and the new PSO with Novelty Search algorithm allow us to obtain a new combustion system that minimizes the fuel consumption by 3%, simultaneously reducing NOx and soot emissions.

Mental Health Analysis in Social Media Posts: A Survey.

The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of social media data bring to fore the importance of real-time responsible AI models for mental health analysis. Aiming to classify the research directions for social computing and tracking advances in the development of machine learning (ML) and deep learning (DL) based models, we propose a comprehensive survey on quantifying mental health on social media. We compose a taxonomy for mental healthcare and highlight recent attempts in examining social well-being with personal writings on social media. We define all the possible research directions for mental healthcare and investigate a thread of handling online social media data for stress, depression and suicide detection for this work. The key features of this manuscript are (i) feature extraction and classification, (ii) recent advancements in AI models, (iii) publicly available dataset, (iv) new frontiers and future research directions. We compile this information to introduce young research and academic practitioners with the field of computational intelligence for mental health analysis on social media. In this manuscript, we carry out a quantitative synthesis and a qualitative review with the corpus of over 92 potential research articles. In this context, we release the collection of existing work on suicide detection in an easily accessible and updatable repository:https://github.com/drmuskangarg/mentalhealthcare.

Optimal Design of Convolutional Neural Network Architectures Using Teaching–Learning-Based Optimization for Image Classification

Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional machine learning techniques in solving various real-life problems in computational intelligence fields, such as image classification. However, most existing CNN architectures were handcrafted from scratch and required significant amounts of problem domain knowledge from designers. A novel deep learning method abbreviated as TLBOCNN is proposed in this paper by leveraging the excellent global search ability of teaching–learning-based optimization (TLBO) to obtain an optimal design of network architecture for a CNN based on the given dataset with symmetrical distribution of each class of data samples. A variable-length encoding scheme is first introduced in TLBOCNN to represent each learner as a potential CNN architecture with different layer parameters. During the teacher phase, a new mainstream architecture computation scheme is designed to compute the mean parameter values of CNN architectures by considering the information encoded into the existing population members with variable lengths. The new mechanisms of determining the differences between two learners with variable lengths and updating their positions are also devised in both the teacher and learner phases to obtain new learners. Extensive simulation studies report that the proposed TLBOCNN achieves symmetrical performance in classifying the majority of MNIST-variant datasets, displays the highest accuracy, and produces CNN models with the lowest complexity levels compared to other state-of-the-art methods due to its promising search ability.

PSO Algorithm-Based Design of Intelligent Education Personalization System.

The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning.

Research on the Design of Public Space in Urban Renewal Based on Multicriteria Cluster Decision-Making

Urban design is a critical technical tool for shaping and intervening in urban space, but it is also developing into a critical governance tool for guiding the orderly development of urban renewal, thereby contributing significantly to its effectiveness. This paper examines the design of public space in urban renewal through the lens of multicriteria group decision-making, introduces urban design governance theory, develops a theoretical framework for instrumentalizing urban design governance to respond to various levels of urban renewal, and investigates strategies for assisting urban renewal through the innovation of governance subjects and semiformal governance tools, in addition to the formal path of combining urban design and planning. Simultaneously, a multicriteria decision-making algorithm is proposed that combines theoretical concepts from the fields of computational intelligence and multicriteria decision-making, adopts a normalization fundamental model to standardize the attribution function, selects valid data information function values to combine into an aggregation function, and then establishes a multicriteria approach to deal with heterogeneous information based on the aggregation function. The experimental results demonstrate that the proposed algorithm is capable of coping with and representing the imprecision and uncertainty inherent in the input data.

CIS Publication Spotlight [Publication Spotlight

Presents summaries of recent books published in the field of computational intelligence.

A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties

In the past decade, considerable advances have been made in the field of computational intelligence and operations research. However, the majority of these optimisation approaches have been developed for deterministically formulated problems, the parameters of which are often assumed perfectly predictable prior to problem-solving. In practice, this strong assumption unfortunately contradicts the reality of many real-world problems which are subject to different levels of uncertainties. The solutions derived from these deterministic approaches can rapidly deteriorate during execution due to the over-optimisation without explicit consideration of the uncertainties. To address this research gap, a deep reinforcement learning based hyper-heuristic framework is proposed in this paper. The proposed approach enhances the existing hyper-heuristics with a powerful data-driven heuristic selection module in the form of deep reinforcement learning on parameter-controlled low-level heuristics, to substantially improve their handling of uncertainties while optimising across various problems. The performance and practicality of the proposed hyper-heuristic approach have been assessed on two combinatorial optimisation problems: a real-world container terminal truck routing problem with uncertain service times and the well-known online 2D strip packing problem. The experimental results demonstrate its superior performance compared to existing solution methods for these problems. Finally, the increased interpretability of the proposed deep reinforcement learning hyper-heuristic has been exhibited in comparison with the conventional deep reinforcement learning methods.

An improved sine–cosine algorithm with dynamic selection pressure

Sine cosine algorithm (SCA) is a newly developed optimization tool that has received increasing attention in the field of computational intelligence. Nevertheless, SCA has a combination of strong exploration and weak exploitation abilities, which undermines the performance of the algorithm. A dynamic selection strategy (DSS) has been proposed in this paper to overcome this limitation. The central idea is that the selection pressure of the parent is dynamically regulated during the course of evolution, and the parent with higher fitness have higher selection probability. Accordingly, the weak exploitation ability of SCA can be enhanced. This approach can be readily applied to other SCA variants as well. Results from systematic experiments conducted on CEC2014 competition benchmark suite show that the proposed DSS method can significantly improve the performance of the SCAs. In addition, the proposed DSS-SCA algorithm has been successfully applied to six engineering design problems, which shows that the algorithm has the potential to solve other practical problems.

Evolution and effectiveness of loss functions in generative adversarial networks

Recently, the evolution of Generative Adversarial Networks (GANs) has embarked on a journey of revolutionizing the field of artificial and computational intelligence. To improve the generating ability of GANs, various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples, and the effectiveness of the loss functions in improving the generating ability of GANs. In this paper, we present a detailed survey for the loss functions used in GANs, and provide a critical analysis on the pros and cons of these loss functions. First, the basic theory of GANs along with the training mechanism are introduced. Then, the most commonly used loss functions in GANs are introduced and analyzed. Third, the experimental analyses and comparison of these loss functions are presented in different GAN architectures. Finally, several suggestions on choosing suitable loss functions for image synthesis tasks are given.

CIS Publication Spotlight [Publication Spotlight

Presents summaries of recent books published in the field of computational intelligence.

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

CIS Publication Spotlight [Publication Spotlight

Presents summaries of recent books published in the field of computational intelligence.

Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System

Adaptive dynamic programming (ADP), which belongs to the field of computational intelligence, is a powerful tool to address optimal control problems. To overcome the bottleneck of solving Hamilton–Jacobi–Bellman equations, several state-of-the-art ADP approaches are reviewed in this paper. First, two model-based offline iterative ADP methods including policy iteration (PI) and value iteration (VI) are given, and their respective advantages and shortcomings are discussed in detail. Second, the multistep heuristic dynamic programming (HDP) method is introduced, which avoids the requirement of initial admissible control and achieves fast convergence. This method successfully utilizes the advantages of PI and VI and overcomes their drawbacks at the same time. Finally, the discrete-time optimal control strategy is tested on a power system.

A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.

Preface

The international conference on Applied Scientific Computational Intelligence using Data Science (ASCI-2020) has been grappled its importance to discuss the research challenges in the field of Data Science by applying scientific techniques in terms of computational intelligence. The initiative has been taken by Department of Computer Applications, Manipal University Jaipur, Rajasthan, India. Manipal University Jaipur (MUJ) was launched in 2011 on an invitation from the Government of Rajasthan, as a self-financed State University. MUJ has redefined academic excellence in the region, with the Manipal way of learning; one that inspires students of all disciplines to learn and innovate through hands on practical experience. In line with Manipal University’s legacy of providing quality education to its students, the campus uses the latest in technology to impart education. Thus, ASCI-2020 drives this legacy of MUJ to join forces and showcase exorbitant research and industry inventiveness, and to recognize and hear from experts in the field of Data Science. Data science is a huge diverted field. Different kind of algorithms, scientific techniques, processes and systems are used in data science to pull out knowledge and insights from Big Data i.e. structured and unstructured data. It is a concept to data analysis; unify statistics, machine learning and their related methods. This conference aims to reveal into advanced methodologies, prototypes, systems, tools, and techniques of data science from academia, industry and government agencies scientists and practitioners. Whether it is information technology or hardware, banking to healthcare, automation and innovation are revolutionary in almost everything. Cities and infrastructure are becoming smarter, health care is being integrated and education is becoming super-focused. The conference will bring together all topics of interest to those who are inclined towards computing and data science using intelligence techniques.ASCI-2020 proceeding has tried to fetch innovative facts and information from academician, research scholars and scientists in terms of their research results and key findings from all the aspects of data science and computational intelligence. The manuscripts of ASCI-2020 has been called for three tracks of data science and computational intelligence. The first track focused on Big Data Management. Then, second track emphasized the Computational Intelligence Techniques, and third track concentrated on Data Science Applications. Subsequently, these tracks has been formulated on the sub themes of Data Science and Computational Intelligence evolved with emerging fields in this present scenario. The sub themes of first track described Heuristic and Nature Inspired Search, Fuzzy and Rough Sets, Reinforcement Learning, ANN and Deep Neural Networks, Auto Encoder, GAN, Transfer Learning, Data Optimization, Data and Network Outsourcing Services. In addition, the second track includes the topics related to Algorithms and Models, Cognitive Computing Development, Business Intelligence and Strategies, Machine Learning and Statistics, Machine Learning Tools and Techniques, Fielded Applications, Generalization as Search, Machine Translation, Data Communication and Intelligence and Natural Language Processing. Finally, track three has been intended on interdisciplinary topics such as Predictive and Statistical Analysis, Application in Computer Vision, Natural Language Processing, Time Series Data, Computational Mathematics, Drug Discovery and Genomic Sequencing, Data Analysis for Improving Defence Security, Cyber Security Analytics, Data Recommendation in Social Networks, Data Analysis of Customer Need in E-commerce, Search Keyword Analysis, Web and Digital Media and Business Analytics in Agriculture.This conference received 235 papers from across the world such as United States, United Kingdom, United Arab Emirates, Norway, Russia, Saudi Arabia, Turkey, Ukraine, Ethiopia, Canada, Morocco, Poland, Uzbekistan, Ghana, Ecuador and Bangladesh etc. Out of 235 manuscripts 91 high quality papers are selected with 38 % accepting ratio. As a whole, 35 international authors submitted their papers, and 350 authors submitted their manuscript from the country. All the manuscripts have been discussed the new findings in the field of data science and computational intelligence. Authors of the proceedings focused on latest trends such as different data models for classification and prediction in various applications. Nevertheless, this proceeding also bring together different theories and paradigm of neural network, fuzzy systems for computational intelligence.List of Editors are available in the pdf.

CIS Publication Spotlight [Publication Spotlight

Presents summaries of recent books published in the field of computational intelligence.

Evolutionary Transfer Optimization - A New Frontier in Evolutionary Computation Research

The evolutionary algorithm (EA) is a nature-inspired population-based search method that works on Darwinian principles of natural selection. Due to its strong search capability and simplicity of implementation, EA has been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional exact mathematical approaches, such as linear programming, quadratic programming, and convex optimization. Despite its great success, it is worth noting that traditional EA solvers start the search from scratch by assuming zero prior knowledge about the task at hand. However, as problems seldom exist in isolation, solving one problem may yield useful information for solving other related problems. There has been growing interest in conducting research on evolutionary transfer optimization (ETO) in recent years: a paradigm that integrates EA solvers with knowledge learning and transfer across related domains to achieve better optimization efficiency and performance. This paper provides an overview of existing works of ETO based on the type of problems being solved by these methods, which are ETO for Optimization in Uncertain Environment, ETO for Multitask Optimization, ETO for Complex Optimization, ETO for Multi/Many-Objective Optimization, and ETO for Machine Learning Applications. The paper also highlights some of the challenges faced in this emerging research field of computational intelligence and discusses some promising future research directions in ETO. It is hoped that the study presented in this paper can help to inspire the development of more advanced ETO methods and applications.