An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Abstract

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network

“…An adaptive multi-objective PSO algorithm based on a hybrid framework of the solution distribution entropy and population spacing information is developed in [69] for improving the convergent speed and precision. An adaptive PSO is proposed in [70] that can find the expected quality of different locations and tune its exploration-exploitation dilemma.…”

Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network

“…The inertia weight parameter is important for optimization. Some of them are inertia weights that have been introduced in research [16][17][18][19][20][21]. In this study, the logarithm decreasing inertia weight (LogDIW) of PSO [22] was used to optimize the CNN hyperparameter.…”