Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.
Swarm intelligence refers to collective intelligence.
Bio-inspired models have taken inspiration from the nature to solve challenging problems in an intelligent manner.
Artificial plant optimization algorithm (APOA) is a novel evolutionary strategy inspired by tree’s growing process.
In this chapter, we present the convergence analysis and applications of particle swarm optimization algorithm.
In: Cao, J., Yang, L.T., Guo, M., Lau, F. (Eds.), Parallel and Distributed Processing and Applications. In: Lecture Notes in Computer Science, vol. 3358. Springer, Berlin, Heidelberg, pp. 893–902. Lee, M., Yu, J., Kim, Y., Kang, C., ...
In most of the MAs, randomization is realized using a uniform or Gaussian distribution. However, this is not the only way to achieve randomization.
Test functions are important to validate and compare the performance of various optimization algorithms.
Advanced inventory management in complex supply chains requires effective and robust nonlinear optimization due to the stochastic nature of supply and demand variations.
Most swarm intelligence algorithms were devised for continuous optimization problems.
A new metaheuristic optimization algorithm, called krill herd (KH), has been recently proposed by Gandomi and Alavi.