A Novel Method to Find Optimal Solution Based on Modified Butterfly Particle Swarm Optimization

Abstract

The proposed work introducing new coefficients and some modern control parameters such as sensitivity (s(t)) and probability of nectar (p(t)) and modification of the conventional parameter (Φ). With presenting these parameters the performance and searching ability of the BF-PSO is significantly increased compared to standered PSO. This new algorithm is inspired by the intelligent behavior of butterfly during the nectar search process. Which clarify a relationship between intelligent network structures of the BF-PSO and the performances. This work pays attention to the sensitivity and the probability of nectar based on the degree of nodes used in BF-PSO. The proposed results indicate the searching performance of the BF-PSO is depended

Impacts of the Load Models on Optimal Planning of Distributed Generation in Distribution System

“…The butterfly swarm based search process investigates the optimal location depending upon the sensitivity of butterfly toward the flower and the probability of nectar. The information about the optimal solution communicates directly or indirectly between all the butterflies by different means of communication intelligence (such as dancing, colors, chemicals, sounds, physical action, and natural processes) [8]. The butterfly leaning based particle swarm optimization algorithm has developed to ascertain the optimal solutions including the random parameters, acceleration coefficients, probability, sensitivity, lbest, and gbest.…”

“…The velocity limits can be set based on the limits of the problem variables. Hence, the function of inertia weight, sensitivity, and probability as a function of iterations can be given as [7,8] = 0.9 − ( (0.9 − 0.4) where ITER max is maximum number of iterations and ITER is th iteration count. And FIT lbest, is fitness of local best solutions with th iteration and FIT gbest, is fitness of global best solutions with th iteration.…”

“…The standard or conventional particle swarm optimization (PSO) based optimization approach is given in [4][5][6]. The butterfly-particle swarm optimization (butterfly-PSO or BF-PSO) technique is based on the characteristic, behavior, and intelligence of the butterfly swarm search process for food, hence attraction towards food (or nectar) source which is described in [7,8]. The simple and efficient method for solving load flow case radial distribution networks proposed by [9] involves only the evaluation of a simple algebraic expression of voltage magnitudes.…”

Assessment of Intericate DG Planning With Practical Load Models by using PSO

“…It's a popular and more effective optimization technique based on the population search. The basic equations of PSO are given as [4][5]:…”

“…The particle swarm optimization (PSO) technique has been described in [4,[7][8]). Many approaches for optimal allocation and sizing of DG in distribution systems are introduced by [3,5,6,9]. The power flow in the system are described by [10].…”