Recent Metaheuristics Analysis of Path Planning Optimaztion Problems

Salawudeen, Ahmed Tijani; Nyabvo, Patrick Julius; Nuhu, Aliyu S.; Akut, Emmanuel K.; Cinfwat, Kishark Zakka; Momoh, Izuagbe S.; Imam, Maryam Lami

doi:10.1109/icmcecs47690.2020.240845

Cited by 8 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results observed shows the SAO is highly efficient and can also compete with meta-heuristic algorithms reported in the literature (Salawudeen et al, 2018). Table 2 shows the pseudo-code for SAO algorithm (Salawudeen et al, 2020). The SAO operates in three distinct modes (Salawudeen et al, 2020):…”

Section: Smell Agent Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Optimal PID Controller Tuning for DC Motor Speed Control Using Smell Agent Optimization Algorithm

Abba¹,

Karataev²,

Thomas³

et al. 2022

FUOYEJET

View full text Add to dashboard Cite

This paper presents an optimal PID controller for speed control of DC motor using Smell Agent Optimization (SAO) Algorithm. DC Motor is an energy converter which transforms electrical energy into mechanical energy used in several industrial applications. A SAO based PID is proposed for optimal tuning of the parameters of the controller to improve speed control of the motor. The SAO uses the phenomenon of smell perception and the intuitive trailing behavior of an agent to identify the source of smell. The integral of time multiplied absolute error (ITAE) is taken as the objective function for obtaining the parameters of the PID controller. It has been observed SAO-PID controller outperforms the GA based PID controller with less settling time, rise time and overshoot. Keywords— DC motor, PID controller, rising time, settling time, smell agent optimization algorithm

show abstract

Section: Smell Agent Optimizationmentioning

confidence: 99%

“…Table 2 shows the pseudo-code for SAO algorithm (Salawudeen et al, 2020). The SAO operates in three distinct modes (Salawudeen et al, 2020):…”

Section: Smell Agent Optimizationmentioning

confidence: 99%

Optimal PID Controller Tuning for DC Motor Speed Control Using Smell Agent Optimization Algorithm

Abba¹,

Karataev²,

Thomas³

et al. 2022

FUOYEJET

View full text Add to dashboard Cite

show abstract

“…al. in [12], proposed an new method for robotic path planning. The proposed method used three meta-heuristic algorithms: Smell Agent Optimization (SAO), PSO and Smell Detection Agent (SDA) to explore the robotic path with least cost and free obstacle.…”

Section: Related Workmentioning

confidence: 99%

A Robotic Path Planning by Using Crow Swarm Optimization Algorithm

Yousif¹,

Salim²,

Jummar³

2021

IJMSC

View full text Add to dashboard Cite

One of the most common problem in the design of robotic technology is the path planning. The challenge is choosing the robotics' path from source to destination with minimum cost. Meta-heuristic algorithms are popular tools used in a search process to get optimal solution. In this paper, we used Crow Swarm Optimization (CSO) to overcome the problem of choosing the optimal path without collision. The results of CSO compared with two meta-heuristic algorithms: PSO and ACO in addition to a hybrid method between these algorithms. The comparison process illustrates that the CSO better than PSO and ACO in path planning, but compared to hybrid method CSO was better whenever the smallest population. Consequently, the importance of research lies in finding a new method to use a new metahumanistic algorithm to solve the problem of robotic path planning.

show abstract