Solving non-linear problems using swarm intelligence | Engineering Online Course by Udemy

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Particle Swarm optimization and its variants

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Solving non-linear problems using swarm intelligence

Due to present scenario of mass customization, industries needs to face several challenges such as dealing simultaneously with multiple conflicting objectives like improving productivity along with quality, improving reliability and efficiency of the manufacturing systems, making manufacturing machines and robots capable to make their own decisions, optimize and enhance resources such as labor, workforce, energy, etc. To overcome these challenges, industries must integrate artificial intelligence in manufacturing through automation, machine learning, artificial intelligence, and cognitive methods. Evolutionary computing being a subset of artificial intelligence has great potential to offer solutions to most of these challenging issues. Evolutionary computation offers solution to the complex problem either through evolutionary process (e.g. genetic algorithm) or through distributed search process ( e.g. swarm intelligence). The swarm intelligence methods include particle swarm optimization, artificial bee colony algorithm, ant colony optimization, shuffled frog leaping algorithm etc. This course is designed to enlighten you about theoretical concepts and practical applications of one of the swarm intelligent methods known as particle swarm optimization. Several variants of particle swarm optimization such as hybrid PSO-SA algorithm, opposition based learning PSO, civilized swarm optimization algorithm etc. are also discussed in detail. These algorithms are explained in step by step manner through simple examples. Moreover, practical case studies are also discussed to demonstrate the applications of these algorithms to real life situations.

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