A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

Al-Rubayi, Rashid H.; Kdair, Mohammed; Flaih, Firas M. F.

doi:10.22266/ijies2020.0229.08

Cited by 11 publications

(5 citation statements)

References 27 publications

(41 reference statements)

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“…new position of a particle is better than the previous value related to the fitness function, then the restoration will occur to the new one as the personal best position. If the value of the new position is better than that of other particles, at this point it will be restored as the global best position [24,25]. The social behavior of some animals in terms of the group's ability to locate a desirable position in the given area was the inspiration to propose the PSO.…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

2022

IJIES

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During the previous years, the number of researchers who discussed the field of path planning algorithms has increased. In this paper, a proposed hybrid technique for path planning based on the benefits of sampling-based algorithms and heuristic path planning algorithms was enhanced. Rapidly-exploring Random Tree star (RRT*) was used to generate the candidate nodes that represent a collision-free path. These candidate nodes were applied to the Particle Swarm Optimization (PSO) algorithm to generate the shortest and the smoothest path planning (the optimal path planning) for the mobile robot. The RRT*PSO algorithm was applied in two scenarios (static and dynamic environments) with a workspace of [500×500] cm using MATLAB 2021a. In the static environment, a workspace full of obstacles was chosen to find the shortest collision-free path. In addition, a reference path equation was found to calculate the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels. In this work, the suggested hybrid RRT*PSO algorithm improves path length 34.27% compared to the A* algorithm and the fuzzy analytic hierarchy process (A*-FAHP hybrid) algorithm and 0.35% compared to the Self-adaptive evolutionary game-based particle swarm optimization (SAEGBPSO) algorithm. In the dynamic environment, an algorithm named (contour path down and contour path up) was suggested to avoid collisions with dynamic obstacles. By using this suggested algorithm, the reference linear and angular velocities of the mobile robot as well as the linear and angular velocities of the right and left wheels were calculated to obtain a smooth path followed and free-navigation with dynamic obstacles in an environment.

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Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

2022

IJIES

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“…The pseudocode of the PSO algorithm is represented in Fig. 3, and the update functions of the velocity and position vectors at the N th iteration [18] can be represented in Eqs. ( 4) and ( 5) whose parameters are represented in Table 2.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

An Intelligent Path Planning Algorithm and Control Strategy Design for MultiMobile Robots based on a Modified Elman Recurrent Neural Network

2022

IJIES

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The goal of the navigation process is to find the optimal path for the mobile robot and control its motion on that path without any oscillation. This work aims to find the optimal paths for multi-mobile robots working in the same static environment. To achieve this goal, the proposed quarter orbits particle swarm optimization (QOPSO) algorithm, which is an enhancement of the cell decomposition algorithm, will be used. The main advantage of using the QOPSO algorithm is to generate the shortest path and avoid collision with static obstacles. Moreover, to direct the motion of the three mobile robots on the desired predefined paths, a proposed inverse differential kinematic neural network trajectory tracking (IDKNNTT) controller based on a modified Elman recurrent neural network (MERNN) will be used. This proposed controller is used to control the nonlinear kinematics mobile robots' system to smoothly and quickly generate the left and right wheels' velocities of the multi mobile robots, which are used to control the orientation and position of each mobile robot. Furthermore, using the proposed controller minimizes the tracking error in the X-axis and the Y-axis positions, approximately zeroes the orientation error, and provides no oscillation in the responses. In particular, the controller guarantees that all the mobile robots will follow their desired paths quickly and correctly. Finally, we validate the numerical simulation results of the proposed control strategy by comparing them to those of other types of controllers in terms of the maximum error enhancement in the X-position and the Y-position. Particularly, when the proposed controller was compared to the convolutional neural network trajectory tracking (CNNTT) controller, the comparison results show that the proposed controller improves the tracking error rate on the X-axis by 75.5 % and enhances the tracking error rate on the Y-axis by 21.2 %. In addition, the proposed controller was compared to the MIMO-PID-MENN controller, and the comparison results show that the proposed controller improves the tracking error rate on the X-axis by 33.3 % and on the Y-axis by 40.6 %.

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“…where: α 0 , approximately from 0.5 to 1, is the initial value of the random parameter. Here t is the number of duplicates or time steps, and γ is a control parameter [19]. For example, in this application, we will use:…”

Section: Accelerating Particle Swarm Optimization Technique (Apso)mentioning

confidence: 99%

Microgrid Optimum Identification Location Based on Accelerated Particle Swarm Optimization Techniques Using SCADA System

Abood¹,

Ali²,

Attia³

et al. 2021

JPEE

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The microgrid has become significant and commonly used; it has localized electricity sources and loads connected to a centralized electrical power network system when the need arises and disconnects to island mode. A microgrid can effectively be integrated with various distribution generators, which can improve the voltage level on the transmission line by reducing the real power losses. In this work, new technologies will permit power grids to be better prepared for future requirements. The numbers and diversity of such decentralized power plants require a new type of management in the operation of power grids and intelligent networks or "smart grid." A SCADA system will improve coordination between power demand and generation and use of modern information technology such as the internet, sensors, controllers, and wireless transmission equipment and use smart metering. The Accelerated Particle Swarm Optimization technique will be used to select the optimum location of a wind turbine to install in the power grid considering minimum power losses with optimal operation consideration of the number of iterations, the execution time of the program, and the memory capacity. The analysis and the study are carried out in MATLAB and the SCADA system.

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A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

Cited by 11 publications

References 27 publications

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

An Intelligent Path Planning Algorithm and Control Strategy Design for MultiMobile Robots based on a Modified Elman Recurrent Neural Network

Microgrid Optimum Identification Location Based on Accelerated Particle Swarm Optimization Techniques Using SCADA System

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