Swarm Intelligence

Preface

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 ...