scholarly journals Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

2021 ◽  
pp. 25-32
Author(s):  
Raed A Hasan ◽  
Suhel Shahab Najim ◽  
Munef Abdullah Ahmed
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Modeling ◽  
Swarm Intelligence ◽  
Low Cost ◽  
Particle Swarm ◽  
Single Species ◽  
Hybrid Swarm ◽  
Fundamental Particle ◽  
Swarm Optimization ◽  
Suboptimal Solutions

A swarm is a group of a single species in which the members interact with one another and with the immediate environment without a principle for control or the emergence of a global intriguing behavior. Swarm-based metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick, robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive at better quality solutions

Bug Report Summarization by Using Swarm Intelligence Approaches

2020 ◽  
Vol 13 (1) ◽  
pp. 53-67
Author(s):  
Ashima Kukkar ◽  
Rajni Mohana
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Feature Weighting ◽  
Training Dataset ◽  
Hybrid Swarm ◽  
Data Set ◽  
Swarm Optimization ◽  
Bug Reports ◽  
Bug Report

Background: Bug reports are considered as a reference document, during the maintenance phase of the software development process. The developer's counsel them at whatever point they have to know about the reported bug and need to explore past bug solution. This process requires a sustainable amount of time due to a large number of comments. Therefore, the best solution to prevent the developers from reading the whole bug report is to summarize the entire discussion in a couple of sentences. Bug report summarization is the extraction of some important part of bug reports that are useful for investigation, resolving the bug with similar problems, reproducing the bug and checking the status of the bug. If the bug reports have huge volume, variety and velocity information as big data, extractive bug report summarization would be emerging as an issue. However, the examiners, find out the bug report summaries do not meet the expectation of the developer; there is a still need for reading the entire discussion. Objective: (1) To generate generalized unsupervised extractive bug report summarization system, which is easily applicable on any dataset without the need of effort and cost of manually creating summaries for training dataset (2) To handle the extensive number of comments and generate short summaries. (3) To reduce the data sparsity, reduction of information, redundancy and convergence issue for short and lengthy data set. (4) To achieve the semantic summarization solution and explore the large search space. (5) To provide the facility of adjusting the summary percentage. Methods: Particle swarm optimization and Hybridization of Ant Colony Optimization and Particle Swarm Optimization approaches are used with the advantage of feature weighting technique. Conclusion: The efficiency of the proposed approaches are compared with the best existing supervised and unsupervised approaches. The result shows that the Hybrid swarm intelligence approach provided better.

From Optimization to Clustering

2015 ◽  
pp. 594-619 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

From Optimization to Clustering

2016 ◽  
pp. 1519-1544 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

scholarly journals Refined Heuristic Swarm Intelligence Algorithm

2020 ◽  
pp. 267-275
Author(s):  
I. I. Aina ◽  
C. N. Ejieji
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.

scholarly journals Enriched Particle Swarm Optimization Created By Varying Parameters for Optimization

2019 ◽  
Vol 8 (3) ◽  
pp. 8259-8265
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Real Life ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Slow Convergence ◽  
Inertia Weight ◽  
Life Problems ◽  
Fixed Constant

Particle swarm optimization (PSO) is one of the most capable algorithms that reside to the swarm intelligence (SI) systems. Recently, it becomes very popular and renowned because of the easy implementation in complex/real life optimization problems. However, PSO has some observable drawbacks such as diversity maintenance, pre convergence and/or slow convergence speed. The ultimate success of PSO depends on the velocity update of the particles. Velocity has a significant dependence on its multiplied coefficient like inertia weight and acceleration factors. To increase the ability of PSO, this paper introduced an enriched PSO (namely ePSO), to solve hard optimization problems more precisely, efficiently and reliably. In ePSO novel gradually decreased inertia weight (as an alternative of a fixed constant value) and new gradually decreased and/or increased acceleration factors (meant for cognitive and social modules) is introduced. Proposed ePSO is used to solve four well known typical unconstrained benchmark functions and four complex unconstrained real life problems. The overall observation shows that proposed new algorithm ePSO is fitter than the compared algorithms significantly and statistically. Moreover, the convergence accuracy and speed of ePSO are also improved effectively

scholarly journals Design Tunable Robust Controllers for Unmanned Aerial Vehicle Based on Particle Swarm Optimization Algorithm

2019 ◽  
Vol 15 (2) ◽  
pp. 89-100
Author(s):  
Baqir Abdul-Samed ◽  
Ammar Aldair
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Low Cost ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Pid Controllers ◽  
Swarm Optimization ◽  
Controlled System ◽  
Main Challenge ◽  
Optimal Values

PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability.

Power Consumption Optimization of a Building Using Multiobjective Particle Swarm Optimization

2015 ◽  
Vol 72 (2) ◽  
Author(s):  
Ahmad Faiz Ab Rahman ◽  
Hazlina Selamat ◽  
Fatimah Sham Ismail ◽  
Nurulaqilla Khamis
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Low Cost ◽  
Particle Swarm ◽  
Sorting Algorithm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Building Energy Optimization ◽  
Save Energy

This paper discusses the development of a building energy optimization algorithm by using multiobjective Particle Swarm Optimization for a building. Particle Swarm Optimization is a well known algorithm that is proven to be effective in many complex optimization problems. Multiobjective PSO is developed by utilizing non-dominated sorting algorithm in tandem with majority-based selection algorithm. The optimizer is written by using MATLAB alongside its GUI interface. Results are then analyzed by using the Binh and Korn benchmark test and natural distance performance metrics. From the results, the optimizer is capable to minimize up to 42 percent of energy consumption and lowering the electricity bills up to 43 percent, while still maintaining comfort at more than 95 percent as well. With this, building owner can save energy with a low-cost and simple solution. 

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