This two-volume set LNCS 13968 and 13969 constitutes the proceedings of the 14th International Conference on Advances in Swarm Intelligence, ICSI 2023, which took place in Shenzhen, China, China, in July 2023. The theme of this year’s conference was “Serving Life with Swarm Intelligence�. The 81 full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized into 12 cohesive sections covering major topics of swarm intelligence research and its development and applications. The papers of the second part cover topics such as: Swarm Robotics and UAV; Machine Learning; Data Mining; Routing and Scheduling Problems; Stock Prediction and Portfolio Optimization; ICSI-Optimization Competition.  

This two-volume set LNCS 13968 and 13969 constitutes the proceedings of the 14th International Conference on Advances in Swarm Intelligence, ICSI 2023, which took place in Shenzhen, China, China, in July 2023. The theme of this year’s conference was “Serving Life with Swarm Intelligence�.The 81 full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized into 12 cohesive sections covering major topics of swarm intelligence research and its development and applications. The papers of the first part cover topics such as: Swarm Intelligence Computing; Swarm Intelligence Optimization Algorithms; Particle Swarm Optimization Algorithms; Genetic Algorithms; Optimization Computing Algorithms; Neural Network Search & Large-Scale Optimization; Multi-objective Optimization.

This two-volume set, CCIS 1453 and CCIS 1454, constitutes refereed proceedings of the 6th International Conference on Data Mining and Big Data, DMBD 2021, held in Guangzhou, China, in October 2021. The 57 full papers and 28 short papers presented in this two-volume set were carefully reviewed and selected from 258 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.

This two-volume set, CCIS 1453 and CCIS 1454, constitutes refereed proceedings of the 6th International Conference on Data Mining and Big Data, DMBD 2021, held in Guangzhou, China, in October 2021. The 57 full papers and 28 short papers presented in this two-volume set were carefully reviewed and selected from 258 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.

Russ Eberhart and Yuhui Shi have succeeded in integrating various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook, including lots of practical examples. -Shun-ichi Amari, RIKEN Brain Science Institute, Japan
This book is an excellent choice on its own, but, as in my case, will form the foundation for our advanced graduate courses in the CI disciplines. -James M. Keller, University of Missouri-Columbia
The excellent new book by Eberhart and Shi asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. The book has an emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. -Xin Yao, The Centre of Excellence for Research in Computational Intelligence and Applications, Birmingham
The "soft" analytic tools that comprise the field of computational intelligence have matured to the extent that they can, often in powerful combination with one another, form the foundation for a variety of solutions suitable for use by domain experts without extensive programming experience.
Computational Intelligence: Concepts to Implementations provides the conceptual and practical knowledge necessary to develop solutions of this kind. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective.
Features
- Movesclearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologies.
- Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation.
- Details the metrics and analytical tools needed to assess the performance of computational intelligence tools.
- Concludes with a series of case studies that illustrate a wide range of successful applications.
- Presents code examples in C and C++.
- Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-study.
- Makes available, on a companion website, a number of software implementations that can be adapted for real-world applications.
- Moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific concepts and paradigms that inform the authors' methodologies.
- Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation.
- Details the metrics and analytical tools needed to assess the performance of computational intelligence tools.
- Concludes with a series of case studies that illustrate a wide range of successful applications.
- Presents code examples in C and C++.
- Provides, at the end of each chapter, review questions and exercises suitable for graduate students, as well as researchers and practitioners engaged in self-study.
- Makes available, on a companionwebsite, a number of software implementations that can be adapted for real-world applications.

Brain Storm Optimization (BSO) algorithms are a new kind of swarm intelligence method, which is based on the collective behavior of human beings, i.e., on the brainstorming process. Since the introduction of BSO algorithms in 2011, many studies on them have been conducted. They not only offer an optimization method, but could also be viewed as a framework of optimization techniques. The process employed in the algorithms could be simplified as a framework with two basic operations: the converging operation and the diverging operation. A "good enough" optimum could be obtained through recursive solution divergence and convergence. The resulting optimization algorithm would naturally have the capability of both convergence and divergence. This book is primarily intended for researchers, engineers, and graduate students with an interest in BSO algorithms and their applications. The chapters cover various aspects of BSO algorithms, and collectively provide broad insights into what these algorithms have to offer. The book is ideally suited as a graduate-level textbook, whereby students may be tasked with the study of the rich variants of BSO algorithms that involves a hands-on implementation to demonstrate the utility and applicability of BSO algorithms in solving optimization problems.

The two-volume set of LNCS 10941 and 10942 constitutes the proceedings of the 9th International Conference on Advances in Swarm Intelligence, ICSI 2018, held in Shanghai, China, in June 2018. The total of 113 papers presented in these volumes was carefully reviewed and selected from 197 submissions. The papers were organized in topical sections as follows: theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithms; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization; multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; prediction, classification; finding patterns; image enhancement; deep learning.

This book constitutes the refereed proceedings of the Third International Conference on Data Mining and Big Data, DMBD 2018, held in Shanghai, China, in June 2018. The 74 papers presented in this volume were carefully reviewed and selected from 126 submissions. They are organized in topical sections named: database, data preprocessing, matrix factorization, data analysis, visualization, visibility analysis, clustering, prediction, classification, pattern discovery, text mining and knowledge management, recommendation system in social media, deep learning, big data, Industry 4.0, practical applications

This volume constitutes the proceedings of the 10th International Conference on Simulated Evolution and Learning, SEAL 2012, held in Dunedin, New Zealand, in December 2014. The 42 full papers and 29 short papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on evolutionary optimization; evolutionary multi-objective optimization; evolutionary machine learning; theoretical developments; evolutionary feature reduction; evolutionary scheduling and combinatorial optimization; real world applications and evolutionary image analysis.

This book and its companion volume, LNCS vol. 8794 and 8795 constitute the proceedings of the 5th International Conference on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 198 submissions. The papers are organized in 18 cohesive sections, 3 special sessions and one competitive session covering all major topics of swarm intelligence research and development such as novel swarm-based search methods; novel optimization algorithm; particle swarm optimization; ant colony optimization for travelling salesman problem; artificial bee colony algorithms; artificial immune system; evolutionary algorithms; neural networks and fuzzy methods; hybrid methods; multi-objective optimization; multi-agent systems; evolutionary clustering algorithms; classification methods; GPU-based methods; scheduling and path planning; wireless sensor networks; power system optimization; swarm intelligence in image and video processing; applications of swarm intelligence to management problems; swarm intelligence for real-world application.

This book and its companion volume, LNCS vol. 8794 and 8795 constitute the proceedings of the 5th International Conference on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 198 submissions. The papers are organized in 18 cohesive sections, 3 special sessions and one competitive session covering all major topics of swarm intelligence research and development such as novel swarm-based search methods; novel optimization algorithm; particle swarm optimization; ant colony optimization for travelling salesman problem; artificial bee colony algorithms; artificial immune system; evolutionary algorithms; neural networks and fuzzy methods; hybrid methods; multi-objective optimization; multi-agent systems; evolutionary clustering algorithms; classification methods; GPU-based methods; scheduling and path planning; wireless sensor networks; power system optimization; swarm intelligence in image and video processing; applications of swarm intelligence to management problems; swarm intelligence for real-world application.

This book and its companion volume, LNCS vols. 7928 and 7929 constitute the proceedings of the 4th International Conference on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised full papers presented were carefully reviewed and selected from 268 submissions. The papers are organized in 22 cohesive sections covering all major topics of swarm intelligence research and developments. The following topics are covered in this volume: analysis of swarm intelligence based algorithms, particle swarm optimization, applications of particle swarm optimization algorithms, ant colony optimization algorithms, biogeography-based optimization algorithms, novel swarm-based search methods, bee colony algorithms, differential evolution, neural networks, fuzzy methods, evolutionary programming and evolutionary games.

This book and its companion volume, LNCS vols. 7928 and 7929 constitute the proceedings of the 4th International Conference on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised full papers presented were carefully reviewed and selected from 268 submissions. The papers are organized in 22 cohesive sections covering all major topics of swarm intelligence research and developments. The topics covered in this volume are: hybrid algorithms, swarm-robot and multi-agent systems, support vector machines, data mining methods, system and information security, intelligent control, wireless sensor network, scheduling and path planning, image and video processing, and other applications.

From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more. It is only in recent years that researchers have taken notice of such natural swarming systems as culmination of some form of innate collective intelligence, albeit swarm intelligence (SI) - a metaphor that inspires a myriad of computational problem-solving techniques. In computational intelligence, swarm-like algorithms have been successfully applied to solve many real-world problems in engineering and sciences. This handbook volume serves as a useful foundational as well as consolidatory state-of-art collection of articles in the field from various researchers around the globe. It has a rich collection of contributions pertaining to the theoretical and empirical study of single and multi-objective variants of swarm intelligence based algorithms like particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), honey bee social foraging algorithms, and harmony search (HS). With chapters describing various applications of SI techniques in real-world engineering problems, this handbook can be a valuable resource for researchers and practitioners, giving an in-depth flavor of what SI is capable of achieving.

This book and its companion volume, LNCS vols. 7331 and 7332, constitute the Proceedings of the Third International conference on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The 145 full papers presented were carefully reviewed and selected from 247 submissions. The papers are organized in 27 cohesive sections covering all major topics of swarm intelligence research and developments.

This book and its companion volume, LNCS vols. 7331 and 7332, constitute the proceedings of the Third International Conference on Swarm Intelligence, ICSI 2012, held in Shenzhen, China in June 2012. The 145 revised full papers presented were carefully reviewed and selected from 247 submissions. The papers are organized in 27 cohesive sections covering all major topics of swarm intelligence research and developments.

The two-volume set (LNCS 6728 and 6729) constitutes the refereed
proceedings of the International Conference on Swarm Intelligence, ICSI
2011, held in Chongqing, China, in June 2011.

The 143 revised full papers presented were carefully reviewed and selected from 298 submissions. The papers are organized in topical sections on theoretical analysis of swarm intelligence algorithms, particle swarm optimization, applications of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, artificial immune system, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy methods, and hybrid algorithms - for part I.

Topics addressed in part II are such as multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent systems, data mining methods, machine learning methods, feature selection algorithms, pattern recognition methods, intelligent control, other optimization algorithms and applications, data fusion and swarm intelligence, as well as fish school search - foundations and applications. "

This two-volume set, CCIS 1744 and CCIS 1745 book constitutes the 7th International Conference, on Data Mining and Big Data, DMBD 2022, held in Beijing, China, in November 21-24, 2022. The 62 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 135 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.

This two-volume set, CCIS 1744 and CCIS 1745 book constitutes the 7th International Conference, on Data Mining and Big Data, DMBD 2022, held in Beijing, China, in November 21-24, 2022. The 62 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 135 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.

This two-volume set LNCS 13344 and 13345 constitutes the proceedings of the 13th International Conference on Advances in Swarm Intelligence, ICSI 2022, which took place in Xi'an, China, in July 2022. The theme of this year's conference was "Serving Life with Swarm Intelligence".The 85 full papers presented were carefully reviewed and selected from 171 submissions. The papers of the first part cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.

This two-volume set LNCS 13344 and 13345 constitutes the proceedings of the 13th International Conference on Advances in Swarm Intelligence, ICSI 2022, which took place in Xi'an, China, in July 2022. The theme of this year's conference was "Serving Life with Swarm Intelligence". The 85 full papers presented were carefully reviewed and selected from 171 submissions. The papers of the second part cover topics such as: Swarm Robotics and Multi-agent System; Deep Neural Networks; Machine Learning; Data Mining; Other Optimization Applications; ICSI-OC'2022: Competition on Single Objective Bounded Optimization Problems; Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Swarm Intelligence Approach-based Applications; Multi-Objective Optimization.

Traditional methods for creating intelligent computational systems have
privileged private "internal" cognitive and computational processes. In
contrast, "Swarm Intelligence" argues that human
intelligence derives from the interactions of individuals in a social world
and further, that this model of intelligence can be effectively applied to
artificially intelligent systems. The authors first present the foundations of
this new approach through an extensive review of the critical literature in
social psychology, cognitive science, and evolutionary computation. They
then show in detail how these theories and models apply to a new
computational intelligence methodology particle swarms which focuses
on adaptation as the key behavior of intelligent systems. Drilling down
still further, the authors describe the practical benefits of applying particle
swarm optimization to a range of engineering problems. Developed by
the authors, this algorithm is an extension of cellular automata and
provides a powerful optimization, learning, and problem solving method.

This important book presents valuable new insights by exploring the
boundaries shared by cognitive science, social psychology, artificial life,
artificial intelligence, and evolutionary computation and by applying these
insights to the solving of difficult engineering problems. Researchers and
graduate students in any of these disciplines will find the material
intriguing, provocative, and revealing as will the curious and savvy
computing professional.
* Places particle swarms within the larger context of intelligent
adaptive behavior and evolutionary computation.
* Describes recent results of experiments with the particle swarm
optimization (PSO) algorithm
* Includes a basic overview of statistics to ensure readers can
properly analyze the results of their own experiments using the
algorithm.
* Support software which can be downloaded from the publishers
website, includes a Java PSO applet, C and Visual Basic source
code."

his two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advances in Swarm Intelligence, ICSI 2021, held in Qingdao, China, in July 2021. The 104 full papers presented in this volume were carefully reviewed and selected from 177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.

his two-volume set LNCS 12689-12690 constitutes the refereed proceedings of the 12th International Conference on Advances in Swarm Intelligence, ICSI 2021, held in Qingdao, China, in July 2021. The 104 full papers presented in this volume were carefully reviewed and selected from 177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for Optimization; Particle Swarm Optimization; Ant Colony Optimization; Differential Evolution; Genetic Algorithm and Evolutionary Computation; Fireworks Algorithms; Brain Storm Optimization Algorithm; Bacterial Foraging Optimization Algorithm; DNA Computing Methods; Multi-Objective Optimization; Swarm Robotics and Multi-Agent System; UAV Cooperation and Control; Machine Learning; Data Mining; and Other Applications.

Brain Storm Optimization (BSO) algorithms are a new kind of swarm intelligence method, which is based on the collective behavior of human beings, i.e., on the brainstorming process. Since the introduction of BSO algorithms in 2011, many studies on them have been conducted. They not only offer an optimization method, but could also be viewed as a framework of optimization techniques. The process employed in the algorithms could be simplified as a framework with two basic operations: the converging operation and the diverging operation. A "good enough" optimum could be obtained through recursive solution divergence and convergence. The resulting optimization algorithm would naturally have the capability of both convergence and divergence. This book is primarily intended for researchers, engineers, and graduate students with an interest in BSO algorithms and their applications. The chapters cover various aspects of BSO algorithms, and collectively provide broad insights into what these algorithms have to offer. The book is ideally suited as a graduate-level textbook, whereby students may be tasked with the study of the rich variants of BSO algorithms that involves a hands-on implementation to demonstrate the utility and applicability of BSO algorithms in solving optimization problems.