Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization

Mostaghim, Sanaz; Teich, Jürgen

doi:10.1109/cec.2004.1331061

Cited by 63 publications

(24 citation statements)

References 10 publications

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“…Clustering is applied on the elite particle in the archive, in the case the maximum archive size is reach and the extra particles will be moved. Hsieh [46] also introduced the clustering method to construct the external archive. In the further work, Mostaghim [47] added a multi-level subdivision scheme iterative division of the search space into subspaces (boxes).…”

Section: (2) External Archivementioning

confidence: 99%

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RhoGTPase Involvement in Breast Cancer Migration and Invasion

Simpson¹

2008

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Section: (2) External Archivementioning

confidence: 99%

“…A turbulence factor, which is a random value to the current position, is added to the position updated equation. Based on a further improvement is proposed in [46]. The optimization process is achieved in two steps [50].…”

Section: Density Metric and Diversity Maintainingmentioning

confidence: 99%

RhoGTPase Involvement in Breast Cancer Migration and Invasion

Simpson¹

2008

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“…If there are multiple (competing) objectives evaluation of the optimality becomes more complicated. There are generally two approaches to multi-objective optimization: 1) combining fitness functions and 2) referring to a Pareto front [9,19,20]. A classical way of combining multiple objectives into a single fitness function is a weighted sum of fitness functions -one from each objectivewhere the result of the overall optimization can depend on the choice of weighting.…”

Section: Metaheuristic Optimization Methodsmentioning

confidence: 99%

Three-Parameter Elliptical Aperture Distributions for Sum and Difference Antenna Patterns Using Particle Swarm Optimization

Densmore¹,

Rahmat‐Samii

2013

PIER

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Abstract-This paper presents a unified analysis of the threeparameter aperture distributions for both sum and difference antenna patterns, suitable for communications or telemetry applications with either a stationary or tracking antenna, and with the parameters automatically determined by Particle-Swarm Optimization (PSO). These distributions can be created, for example, by reflector, phased array, or other antenna systems. The optimizations involve multiple objectives, for which Pareto efficiency concepts apply, and are accelerated by compact, analytical closed-form equations for key metrics of the distributions, including the far-field radiation pattern and detection slope of the difference pattern. The limiting cases of the three-parameter distributions are discussed and shown to generalize other distributions in the literature. A derivation of the generalized vector far fields provides the background for the distribution study and helps clarify the definition of cross-polarization in the farfield. Examples are given to show that the three-parameter (3P) distributions meet a range of system-level constraints for various applications, including a sidelobe mask for satellite ground stations and maximizing pointing error detection sensitivity while minimizing clutter from sidelobes for tracking applications. The equations for the relative angle sensitivity for the difference pattern are derived. A study of the sensitivity of the 3P parameter values is presented.

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“…They also introduced a new method which uses the property of moving particles in MOPSO to divide the population into sub-swarms (Mostaghim and Teich, 2004), trying to cover the gaps between the non-dominated solutions found in the initial run. Wei and Wang (2006) proposed a novel MOPSO algorithm in which a three-parent crossover operator was suggested in order to move the solutions toward the feasible region, and a dynamically changing inertia weight was designed to keep the diversity of the swarm and escape from local optima.…”

Section: Introductionmentioning

confidence: 99%

f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management

Rezaei

Safavi

Mirchi

et al. 2017

Journal of Hydro-environment Research

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In recent years, evolutionary techniques have been widely used to search for the global optimum of combinatorial non-linear non-convex problems. In this paper, we present a new algorithm, named fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO) to improve conjunctive surface water and groundwater management. The f-MOPSO algorithm is simple in concept, easy to implement, and computationally efficient. It is based on the role of weighting method to define partial performance of each point (solution) in the objective space.The proposed algorithm employs a fuzzy inference system to consider all the partial 2 performances for each point when optimizing the objective function values. The f-MOPSO algorithm was compared with two other well-known MOPSOs through a case study of conjunctive use of surface and groundwater in Najafabad Plain in Iran considering two management models, including a typical 12-month operation period and a 10-year planning horizon. Overall, the f-MOPSO outperformed the other MOPSO algorithms with reference to performance criteria and Pareto-front analysis while nearly fully satisfying water demands with least monthly and cumulative groundwater level (GWL) variation. The proposed algorithm is capable of finding the unique optimal solution on the Pareto-front to facilitate decisions to address large-scale optimization problems.

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Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization

Cited by 63 publications

References 10 publications

RhoGTPase Involvement in Breast Cancer Migration and Invasion

RhoGTPase Involvement in Breast Cancer Migration and Invasion

Three-Parameter Elliptical Aperture Distributions for Sum and Difference Antenna Patterns Using Particle Swarm Optimization

f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management

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