Global and Local Neighborhood Based Particle Swarm Optimization Shakti Chourasia, Harish Sharma, Manoj Singh and Jagdish Chand Bansal Abstract The particle swarm optimization (PSO) is one of the popular and simple to implement swarm intelligence based algorithms. To some extent, PSO dominates other optimization algorithms but prematurely converging to local optima and stag- nation in later generations are some pitfalls. The reason for these problems is the unbalancing of the diversification and convergence abilities of the population during the solution search process. In this paper, a novel position update process is developed and incorporated in PSO by adopting the concept of the neighborhood topologies for each particle. Statistical analysis over 15 complex benchmark functions shows that performance of propounded PSO version is much better than standard PSO (PSO 2011) algorithm while maintaining the cost-effectiveness in terms of function evaluations. Keywords Swarm intelligence based algorithm · Nature-inspired algorithm Neighborhood topology · Optimization 1 Introduction Kennedy and Eberhart in 1995 [4, 9] examined that the swarm intelligence is showed by the flocking of birds and schooling of fishes, inspiring from which an optimization technique was introduced by them which was called the particle swarm optimization S. Chourasia · M. Singh Gurukul Institute of Engineering & Technology, Kota, India e-mail: shakti.engg85@gmail.com M. Singh e-mail: manojsinghq100@yahoo.com H. Sharma (B ) Rajasthan Technical University, Kota, India e-mail: harish.sharma0107@gmail.com; hsharma@rtu.ac.in J. C. Bansal South Asian University, New Delhi, India e-mail: jcbansal@sau.ac.in © Springer Nature Singapore Pte Ltd. 2019 N. Yadav et al. (eds.), Harmony Search and Nature Inspired Optimization Algorithms, Advances in Intelligent Systems and Computing 741, https://doi.org/10.1007/978-981-13-0761-4_44 449