Stochastic Set-Based Particle Swarm Optimization Based on Local Exploration for Solving the Carpool Service Problem

Abstract

The growing ubiquity of vehicles has led to increased concerns about environmental issues. These concerns can be mitigated by implementing an effective carpool service. In an intelligent carpool system, an automated service process assists carpool participants in determining routes and matches. It is a discrete optimization problem that involves a system-wide condition as well as participants' expectations. In this paper, we solve the carpool service problem (CSP) to provide satisfactory ride matches. To this end, we developed a particle swarm carpool algorithm based on stochastic set-based particle swarm optimization (PSO). Our method introduces stochastic coding to augment traditional particles, and uses three terminologies to represent a particle: 1) particle position; 2) particle view; and 3) particle velocity. In this way, the set-based PSO (S-PSO) can be realized by local exploration. In the simulation and experiments, two kind of discrete PSOs-S-PSO and binary PSO (BPSO)-and a genetic algorithm (GA) are compared and examined using tested benchmarks that simulate a real-world metropolis. We observed that the S-PSO outperformed the BPSO and the GA thoroughly. Moreover, our method yielded the best result in a statistical test and successfully obtained numerical results for meeting the optimization objectives of the CSP.

Secure Massive MIMO System with Two-Way Relay Cooperative Transmission in 6G Networks

“…The comparisons of QCRO algorithm, particle swarm optimization (PSO) algorithm [40], chemical reaction optimization (CRO) algorithm [39], single-relay selection (SRS) strategy [32], and random multiple-relay selection (RMRS) strategy on the security performance are presented in Figs. 1 -5.…”

“…For comparison purpose, we set the maximum number of iterations for QCRO, PSO, and CRO algorithms to the same value, and all these algorithms are set to the same population size. The other parameters of PSO and CRO algorithms are set to the optimal values cited in [40] and [39], respectively. For QCRO algorithm, all quantum bits are initialized to 0.5 and the initial kinetic energy each quantum molecule is set to 1000.…”

Ensemble learning particle swarm optimization for real-time UWB indoor localization

“…Applications in communication consist of routing optimization [35], wireless communication system optimization [36], filter design optimization [37], etc. Relatively, there are very limited study or applications in biology [38], artificial intelligence [39,40], and some other crossing fields [41][42][43]. In this work, we focus on the area of artificial intelligence and propose a novel PSO algorithm applied in UWB indoor localization.…”

Parameter Estimation of Electromechanical Oscillation Based on a Constrained EKF with C&I-PSO

“…Until now, various solutions have been developed to address these optimization problems. The most popular method of them is particle swarm optimization (PSO) [29][30][31][32], which has been widely used in many optimization problems. It has the ability to solve difficult optimization problems and converge quickly to a solution [33][34][35][36].…”