Comparing with Chaotic Inertia Weights in Particle Swarm Optimization

Feng, Yong; Yao, Yong-Mei; Wang, Ai-Xin

doi:10.1109/icmlc.2007.4370164

Cited by 39 publications

(16 citation statements)

References 4 publications

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“…Since inertia weight was proposed, many researchers have made contributions in this field. Some classical forms of inertia weight have been proposed, such as time invariant [15], linear time variant [16,30], nonlinear time variant [17,31], and other forms of inertia weight [32][33][34]. e famous forms of inertia weight mentioned above are described in detail in the subsections below.…”

Section: Different Forms Of Inertia Weightmentioning

confidence: 99%

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Zhang

Sheng

et al. 2021

Computational Intelligence and Neuroscience

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The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.

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Section: Different Forms Of Inertia Weightmentioning

confidence: 99%

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Zhang

Sheng

et al. 2021

Computational Intelligence and Neuroscience

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“…Feng et al [27][28] proposed Chaotic Inertia Weight using the merits of chaotic optimization. It found that the CRIW enhances the performance of PSO in comparison with RIW.…”

Section:  mentioning

confidence: 99%

Impact of inertia weight strategies in particle swarm optimization for solving economic dispatch problem

Meziane¹,

Mouloudi²,

Bouchiba³

et al. 2019

IJEECS

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<p>Particle Swarm Optimization (PSO) is a population based stochastic optimization technique inspired by the social learning of birds or fish. Some of the appealing facts of PSO are its convenience, simplicity and easiness of implementation requiring but few parameters adjustments. Inertia Weight (ω) is one of the essential parameters in PSO, which often significantly the affects convergence and the balance between the exploration and exploitation characteristics of PSO. Since the adoption of this parameter, there have been large proposals for determining the value of Inertia Weight Strategy. In order to show the efficiency of this parameter in the Economic Dispatch problem(ED), this paper presents a comprehensive review of one or more than one recent and popular inertia weight strategies reported in the related literature. Among this five recent inertia weight four were randomly chosen for application and subject to empirical studies in this research, namely, Constant (ω), Random (ω), Global-Local Best (ω), Linearly Decreasing (ω), which are then compared in term of performance within the confines of the discussed optimization problem. Morever, the results are compared to those reported in the recent literature and data from SONELGAZ. The study results are quite encouraging showing the good applicability of PSO with adaptive inertia weight for solving economic dispatch problem.</p>

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“…dynamical adjusting of inertia weight increase capability of PSO. As yet many methods for calculating inertia weight presented: linearly increasing and decreasing strategy [8], fuzzy inertia weight strategy, random and constant inertia weight strategy [4,7], inertia weight with adaptive chaos [6,9],particles rank strategy [10] .in 2009 Ebadzade and Nikabadi [7] present a strategy based on success rate of particle and feedback , this strategy can be adaptive to step size to find a good solution in enough generation and is a best method to solve a multi and dynamic objects [7]. All strategies categorized in there methods : constant and random inertia weight, time varying inertia weight, adaptive inertia weight [ 7].methods of inertia weight used in this paper , categorized in table 1.…”

Section: -Inertia Weightmentioning

confidence: 99%

Introduce a New Inertia Weight for Particle Swarm Optimization

Ememipour

Nejad

Ebadzadeh

et al. 2009

2009 Fourth International Conference on Computer Sciences and Convergence Information Technology

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PSO has a few parameters to adjust such as inertia weight, velocity and constant factors. Among these parameters, inertia weight is very important and has a great potential to develop. During the last decade, various methods like fuzzy, constant, linear methods were proposed to adjust an inertia weight. This paper proposes a new strategy to calculate inertia weight based on decreasing exponential method. Our method merely uses an iteration to make an inertia weight and it is fast and has highly accurate results rather than other strategies. Our results are tested on well-known benchmarks. Numerical results demonstrated our claim.Particle swarm optimization (PSO) is a population based technique first introduced by Kennedy and Eberhart in 1995[1]. PSO is based on the simulation of simplified animal social behaviors such as fish schooling, bird flocking, etc. Instead of using evolutionary operators to manipulate the individuals, PSO relies on the exchange of information between individuals [2]. The PSO method is becoming very popular due to its simplicity of implementation and ability to quickly to reach a reasonable solution [3]. Much of the work in PSO cares about adjusting inertia weight [3]. Shi at al proposed linearly decreasing inertia weight strategy, fuzzy inertia weight strategy and random inertia weight strategy.Results show that inertia weight is an important parameter in PSO algorithm. Great values of inertia weight could improve global exploration and small ones could improve local exploration. Unlike a GA, PSO does not use an evolution operator such as crossover and mutation [4]. We present a new strategy based on time varying using an iteration and compare with other strategy and numerical result show that this strategy is good in low generation and high accuracy.The rest of this paper is organized as follow: the next section introduce Basic PSO. A time varying strategy of inertia weight and mathematical proof of inertia weight presented in section 3. In section 4 new strategy and its description is presented, we describe the test functions and the numerical results and comparison with other methods in section 5, finally in section 6 we present summarization and conclude of our work. 2-Basic PSOIn PSO, each individual called particle a number in the swarm, called a particle and represent a potential solution that is a point in the search space the global optimum is regarded as the location of food .all particle named swarm. firstly , the PSO algorithm generate the particle of the population and initial velocity of each particle randomly then it modify the velocity and the best fitness value of each particle iteratively to search the optimum of the problem .each particle record two values, record the optimum by the particle itself and records the optimum of all particles in the population. each particle has a fitness value and a velocity to adjust its flying direction according to the best experiences of the swarm to

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Comparing with Chaotic Inertia Weights in Particle Swarm Optimization

Cited by 39 publications

References 4 publications

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

Impact of inertia weight strategies in particle swarm optimization for solving economic dispatch problem

Introduce a New Inertia Weight for Particle Swarm Optimization

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