Coverage Optimization Methods in Wireless Homo-Sensor Network Based on Guided Swarms

2011 ◽  
Vol 148-149 ◽  
pp. 868-874
Author(s):  
Huan Yang Zheng
Keyword(s):  
Sensor Network ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Search Process ◽  
Migration Process ◽  
Swarm Optimization ◽  
Network Position ◽  
Coverage Optimization ◽  
Position Problem ◽  
Grid Based

An improved particle swarm optimization (PSO) algorithm is designed for the grid based wireless homo-sensor network position problem. The proposed method, called guided method, introduces the simulation of migration process to PSO and its mutation algorithm, using a previous designed sparse position plan to guide the swarm to the optimization solution, and accelerates the search process. Experiments show not only the feasibility and validity of the proposed method but also a marked improvement in performance over traditional PSO.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

A Glowworm Swarm Optimization Algorithm for Uninhabited Combat Air Vehicle Path Planning

2015 ◽  
Vol 24 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Zhonghua Tang ◽  
Yongquan Zhou
Keyword(s):  
Path Planning ◽  
Heuristic Algorithms ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Population Based ◽  
Computational Accuracy ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Air Vehicle ◽  
The Individual

AbstractUninhabited combat air vehicle (UCAV) path planning is a complicated, high-dimension optimization problem. To solve this problem, we present in this article an improved glowworm swarm optimization (GSO) algorithm based on the particle swarm optimization (PSO) algorithm, which we call the PGSO algorithm. In PGSO, the mechanism of a glowworm individual was modified via the individual generation mechanism of PSO. Meanwhile, to improve the presented algorithm’s convergence rate and computational accuracy, we reference the idea of parallel hybrid mutation and local search near the global optimal location. To prove the performance of the proposed algorithm, PGSO was compared with 10 other population-based optimization methods. The experiment results show that the proposed approach is more effective in UCAV path planning than most of the other meta-heuristic algorithms.

An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory

Kybernetes ◽  
2016 ◽  
Vol 45 (2) ◽  
pp. 210-222 ◽  
Author(s):  
Qichang Duan ◽  
Mingxuan Mao ◽  
Pan Duan ◽  
Bei Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Memory Factor ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

Purpose – The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed. Design/methodology/approach – In PSOEM-FSA, the extended memory for PSO is introduced to store each particle’ historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory factor is applied to five kinds of behavior pattern for FSA, including swarming, following, remembering, communicating and searching. Findings – The paper proposes a new intelligent algorithm. On the one hand, this algorithm makes the fish swimming have the characteristics of the speed of inertia; on the other hand, it expands behavior patterns for the fish to choose in the search process and achieves higher accuracy and convergence rate than PSO-FSA, owning to extended memory beneficial to direction and purpose during search. Simulation results verify that these improvements can reduce the blindness of fish search process, improve optimization performance of the algorithm. Research limitations/implications – Because of the chosen research approach, the research results may lack persuasion. In the future study, the authors will conduct more experiments to understand the behavior of PSOEM-FSA. In addition, there are mainly two aspects that the performance of this algorithm could be further improved. Practical implications – The proposed algorithm can be used to many practical engineering problems such as tracking problems. Social implications – The authors hope that the PSOEM-FSA can increase a branch of FSA algorithm, and enrich the content of the intelligent algorithms to some extent. Originality/value – The novel optimized FSA algorithm proposed in this paper improves the convergence speed and searching precision of the ordinary FSA to some degree.

An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

2020 ◽  
pp. 2150001
Author(s):  
Wei-Der Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Design Variables ◽  
Constrained Conditions ◽  
Engineering Optimization Problems

Engineering optimization problems can be always classified into two main categories including the linear programming (LP) and nonlinear programming (NLP) problems. Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system. This paper will focus on the issue about the design problem of NLP with the constrained conditions. The employed method for such NLP problems is a variant of particle swarm optimization (PSO), named improved particle swarm optimization (IPSO). The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems. In this work, many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm. Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.

scholarly journals Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships

2020 ◽  
Vol 10 (20) ◽  
pp. 7314
Author(s):  
Mutaz Ryalat ◽  
Hazem Salim Damiri ◽  
Hisham ElMoaqet
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Population Based ◽  
Dynamic Positioning ◽  
Swarm Optimization ◽  
Integral Action ◽  
Engineering Problems ◽  
Control Research

Dynamic positioning (DP) control system is an essential module used in offshore ships for accurate maneuvering and maintaining of ship’s position and heading (fixed location or pre-determined track) by means of thruster forces being generated by controllers. In this paper, an interconnection and damping assignment-passivity based control (IDA-PBC) controller is developed for DP of surface ships. The design of the IDA-PBC controller involves a dynamic extension utilizing the coordinate transformation which adds damping to some coordinates to ensure asymptotic stability and adds integral action to enhance the robustness of the system against disturbances. The particle swarm optimization (PSO) technique is one of the the population-based optimization methods that has gained the attention of the control research communities and used to solve various engineering problems. The PSO algorithm is proposed for the optimization of the IDA-PBC controller. Numerical simulations results with comparisons illustrate the effectiveness of the new PSO-tuned dynamic IDA-PBC controller.

scholarly journals Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

2021 ◽  
Vol 12 (2) ◽  
pp. 736
Author(s):  
Kareem G. Abdulhussein ◽  
Naseer M. Yasin ◽  
Ihsan J. Hasan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Modern Method ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Permanent Magnet Dc Motor ◽  
Tracking Trajectory

In this paper, two optimization methods are used to adjust the gain values for the cascade PID controller. These algorithms are the butterfly optimization algorithm (BOA), which is a modern method based on tracking the movement of butterflies to the scent of a fragrance to reach the best position and the second method is particle swarm optimization (PSO). The PID controllers in this system are used to control the position, velocity, and current of a permanent magnet DC motor (PMDC) with an accurate tracking trajectory to reach the desired position. The simulation results using the Matlab environment showed that the butterfly optimization algorithm is better than the particle swarming optimization (PSO) in terms of performance and overshoot or any deviation in tracking the path to reach the desired position. While an overshoot of 2.557% was observed when using the PSO algorithm, and a position deviation of 7.82 degrees was observed from the reference position.

scholarly journals A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process

2019 ◽  
Vol 8 (3) ◽  
pp. 1117-1127
Author(s):  
Teh Muy Shin ◽  
Asrul Adam ◽  
Amar Faiz Zainal Abidin
Keyword(s):  
Optimization Methods ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Surface Grinding ◽  
Grinding Process ◽  
Swarm Optimization ◽  
Optimum Parameters ◽  
Maximum Production Rate ◽  
The Cost ◽  
Selection Of

The selection of parameters in grinding process remains as a crucial role to guarantee that the machined product quality is at the minimum production cost and maximum production rate. Therefore, it is required to utilize more advance and effective optimization methods to obtain the optimum parameters and resulting an improvement on the grinding performance. In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. The efficiency of the three algorithms are evaluated and comparedwith previous results obtained by other optimization methods on similar studies.The experimental results showed that PSO algorithm achieves better optimization performance in the aspect of convergence rate and accuracy of best solution.Whereas in the comparison of results of previous researchers, the obtained result of PSO proves that it is efficient in solving the complicated mathematical model of surface grinding process with different conditions.

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