Particle swarm optimization

Poli, Roberto; Kennedy, James; Blackwell, Tim

doi:10.1007/s11721-007-0002-0

Cited by 7,891 publications

(1,988 citation statements)

References 66 publications

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“…On the contrary, in the local best model, the neighbourhood of a particle is defined by several fixed particles. Poli et al [94] stated that the global best model converges faster than the local best model whereas the former model has a higher probability of getting stuck in local optima than the latter model. Surveys of different PSO variations can be found in [95,96].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

490

213

View full text Add to dashboard Cite

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“…The particles are "guided" towards the optimal solution. In fact, the updating rules for the position and the velocity of each particle are meant to simulate "social" interactions between individuals [19]. More precisely, according to the most general formulation of the PSO algorithm, the velocity of particle i is updated, at each iteration, according to the following rule [20]:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Finally, y (i) (t) and y(t) are the positions of the i−th particle with the best fitness function and the position of the particle with the best (among all particles) fitness function reached until instant t [19]. The idea behind the updating rule (11) for the velocities is to add to the previous velocity of each particle in the swarm (weighted by means of a multiplicative factor ω(t)) a stochastic combination of the direction to its best position (corresponding to the second addend in (11)) and to the global best position (third addend in (11)).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Swarm Intelligence Approach to $$3$$D Distance-Based Indoor UWB Localization

Monica

Ferrari

2015

Applications of Evolutionary Computation

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Abstract. In this paper, we focus on the application of Ultra Wide Band (UWB) technology to the problem of locating static nodes in threedimensional indoor environments, assuming to know the positions of a few nodes, denoted as "beacons." The localization algorithms which are considered throughout the paper are based on the Time Of Arrival (TOA) of signals traveling between pairs of nodes. In particular, we propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the localization problem and we compare its performance with that of the Two-Stage Maximum-Likelihood (TSML) algorithm. Simulation results show that the former allows achieving accurate position estimates even in scenarios where, because of ill-conditioning problems associated with the network topology, TSML fails.

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“…Initially, PSO inspired from flocks of birds, schools of fish, and even human social behavior. The nature-based meta-heuristic algorithm was proposed by Kennedy and Eberhart [21], [27]. They simulated a group of birds that are looking for food within some area and they don't know food location.…”

Section: A Particle Swarm Optimizationmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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Abstract-In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.Index Terms-Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.

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Particle swarm optimization

Cited by 7,891 publications

References 66 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A Swarm Intelligence Approach to $$3$$D Distance-Based Indoor UWB Localization

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

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