A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight

Xin, Jianbin; Chen, Guimin; Hai, Y.

doi:10.1109/cso.2009.420

Cited by 171 publications

(92 citation statements)

References 7 publications

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“…Earlier approaches [6][7][8] were mainly focused on the variation of inertia weight to increase the efficiency of PSO. However, they normally used fixed acceleration constants 1 and 2 ( 1 = 2 = 2 ) during the course of iteration.…”

Section: Related Workmentioning

confidence: 99%

“…Linear Decreasing Inertia Weight (LDIW) [6][7][8] is very popular and efficient technique in improving the finetuning characteristics of the PSO where the value of inertia weight is linearly depend on the iteration number. In case of LDIW, the value of is linearly decreased from an initial large value ( ) to a final small value ( ) according to the following equation:…”

Section: Related Workmentioning

confidence: 99%

“…Several authors [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] proposed different methods to achieve better accuracy and convergence. However, in this paper, we have proposed a Modified Particle Swarm Optimization (MPSO) Algorithm based on self-adaptive acceleration constants.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

Mandal¹

2017

IJMECS

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Abstract-Particle Swarm Optimization (PSO) is one of most widely used metaheuristics which is based on collective movement of swarm like birds or fishes. The inertia weight (w) of PSO is normally used for maintaining balance between exploration and exploitation capability. Many strategies for updating the inertia weight during iteration were already proposed by several researchers. In this paper, a Modified Particle Swarm Optimization (MPSO) algorithm based on self-adaptive acceleration constants along with Linear Decreasing Inertia Weight (LDIW) technique is proposed. Here, in spite of using fixed values of acceleration constants, the values are updated themselves during iteration depending on local and global best fitness value respectively. Six different benchmark functions and three others inertia weight strategies were used for validation and comparison with this proposed model. It was observed that proposed MPSO algorithm performed better than others three strategies for most of functions in term of accuracy and convergence although its execution time was larger than others techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

Mandal¹

2017

IJMECS

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“…The cognitive component is the force that pulls the particle to its best position found thus far, and the social component attracts the particle towards the bestsolution found among all the particles. The inertia weight [21,22] and acceleration constant , are assumed to be 0.9…. 0.5and 2 and 2, respectively, and , are the uniformly generated random numbers in the range of (0,1).…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…For the simulation results, the following parameter values are used:inertia coefficient, ω is set to 0.9.... 0.5 [21] and the c 1 , c 2 coefficients are both set to 2, 2. An initial v for each robot is set to simulate the behaviour of the physical robot.…”

Section: Simulation Conditionsmentioning

confidence: 99%

A Hybrid of Modified PSO and Local Search on a Multi-Robot Search System

Rastgoo

Nakisa

Nazri

2015

International Journal of Advanced Robotic Systems

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Particle swarm optimization (PSO), a new populationbased algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area. Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms, a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.

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Optimal scheduling workflows in cloud computing environment using Pareto‐based Grey Wolf Optimizer

Khalili

Babamir

2017

Concurrency and Computation

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A workflow consists of dependent tasks, and scheduling of a workflow in a cloud environment means the arrangement of tasks of the workflow on virtual machines (VMs) of the cloud. By increasing VMs and the diversity of task size, we have a huge number of such arrangements.Finding an arrangement with minimum completion time among all of the arrangements is an Non-Polynomial-hard problem. Moreover, the problem becomes more complex when a scheduling should consider a couple of conflicting objectives. Therefore, the heuristic algorithms have been paid attention to figure out an optimal scheduling. This means that although the singleobjective optimization, ie, minimizing completion time, proposes the workflow scheduling as an NP-complete problem, multiobjective optimization for the scheduling problem is confronted with a more permutation space because an optimal trade-off between the conflicting objectives is needed. To this end, we extended a recent heuristic algorithm called Grey Wolf Optimizer (GWO) and considered dependency graph of workflow tasks. Our experiment was carried out using the WorkflowSim simulator, and the results were compared with those of 2 other heuristic task scheduling algorithms.to optimize some conflicting objectives stated previously in the cloud computing environment. We called it Pareto-based GWO (PGWO) and evaluated its performance on 2 patterns of workflows.Similar to the PSO algorithm, the GWO algorithm is based on swarm intelligence proposed by Mirjalili et al. 6 To evaluate the performance of the proposed algorithm practically, we implemented the extend algorithm using the WorkflowSim tool, 7 which is based on the CloudSim tool. Results of the proposed algorithm were compared with those of Strength Pareto Evolutionary Algorithm 2 (SPEA2) algorithm. 8The rest of the article is organized as follows. In Section 2, related work is presented in the greedy, single, and multiobjectives categories.In Section 3, first we introduce the concept of task scheduling and then we present our method in 2 parts, the extension of (1) the single-objective optimization to multiobjective optimization (MOO) and (2) the GWO algorithm for support of MOO. In Section 4, experimental results, simulation environment, performance indices, and results of the proposed algorithm are evaluated. Finally, conclusions are drawn in Section 5.

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A Particle Swarm Optimizer with Multi-stage Linearly-Decreasing Inertia Weight

Cited by 171 publications

References 7 publications

A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

A Hybrid of Modified PSO and Local Search on a Multi-Robot Search System

Optimal scheduling workflows in cloud computing environment using Pareto‐based Grey Wolf Optimizer

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