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This important authored book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, Artificial Intelligence, and evolutionary computation by applying these insights to solving complex engineering problems.
Motivated by the capability of the biologically inspired algorithms, “Swarm Intelligence: An Approach from Natural to Artificial” focuses on ant, cat, crow, elephant, grasshopper, water wave and whale optimization, swarm cyborg and particle swarm optimization, and presents recent developments and applications concerning optimization with Swarm Intelligence techniques. The goal of the book is to offer a wide spectrum of sample works developed in leading research throughout the world about innovative methodologies of Swarm Intelligence and foundations of engineering swarm intelligent systems as well as applications and interesting experiences using particle swarm optimization, which is at the heart of Computational Intelligence.
Discussed in the book are applications of various swarm intelligence models to operational planning of energy plants, modeling, and control of robots, organic computing, techniques of cloud services, bioinspired optimization, routing protocols for next-generation networks inspired by collective behaviors of insect societies and cybernetic organisms.
In the era of globalization, the emerging technologies are governing engineering industries towards a multifaceted state. The escalating complexity brought about by these new technologies has led to a new set of problems therefore, there has been a demand for researchers to find possible ways to address any new issues that arise. This has motivated researchers to appropriate ideas from nature to implant in the engineering sciences. This way of thinking has led to the emergence of many biologically inspired algorithms, such as genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO), that have proven to be efficient in handling computationally complex problems with competence.
Motivated by the capability of the biologically inspired algorithms, this book on Swarm Intelligence (SI) presents recent developments and applications concerning optimization with SI techniques based on ant, cat, crow, elephant, grasshopper, water wave, whale, swarm cyborg and particle swarm optimization. Particle swarm optimization, commonly known as PSO, mimics the behavior of a swarm of insects or a school of fish. If one of the particles discovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away from the swarm. Swarm behavior is modeled by particles in multidimensional space that has two characteristics: a position and a velocity. These particles wander around the hyperspace and remember the best position that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions. Ant colony optimization, commonly known as ACO, is a probabilistic technique for solving hard computational problems which can be reduced to finding optimal paths.
Audience
The book is directed to researchers, practicing engineers, and students in computational intelligence who are interested in enhancing their knowledge of techniques and Swarm Intelligence