A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization

Kaveh, A.; Laknejadi, K.

doi:10.1016/j.eswa.2011.06.012

Cited by 89 publications

(27 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The metric of MS measures how 'well' the PF optimal is covered by the PF g through hyperboxes formed by the extreme function values observed in the PF optimal and PF g . It is defined as follows (Kaveh & Laknejadi, 2011): where f i max and f i min are the maximum and minimum of the i th objective in the PF optimal , respectively, F i max and F i min are the maximum and minimum of the i th objective in the PF optimal , respectively. Larger value of MS implies a better spread of solutions.…”

Section: Performance Metrics For Mopsmentioning

confidence: 99%

The application of water cycle algorithm to portfolio selection

Moradi

Sadollah

Eskandar

et al. 2017

Economic Research-Ekonomska Istraživanja

View full text Add to dashboard Cite

Portfolio selection is one of the most vital financial problems in literature. The studied problem is a nonlinear multi-objective problem which has been solved by a variety of heuristic and metaheuristic techniques. In this article, a metaheuristic optimiser, the multiobjective water cycle algorithm (MOWCA), is represented to find efficient frontiers associated with the standard mean-variance (M-V) portfolio optimisation model. The inspired concept of WCA is based on the simulation of water cycle process in the nature. Computational results are obtained for analyses of daily data for the period January 2012 to December 2014, including S&P100 in the US, Hang Seng in Hong Kong, FTSE100 in the UK, and DAX100 in Germany. The performance of the MOWCA for solving portfolio optimisation problems has been evaluated in comparison with other multi-objective optimisers including the NSGA-II and multiobjective particle swarm optimisation (MOPSO). Four well-known performance metrics are used to compare the reported optimisers. Statistical optimisation results indicate that the applied MOWCA is an efficient and practical optimiser compared with the other methods for handling portfolio optimisation problems.

show abstract

Section: Performance Metrics For Mopsmentioning

confidence: 99%

The application of water cycle algorithm to portfolio selection

Moradi

Sadollah

Eskandar

et al. 2017

Economic Research-Ekonomska Istraživanja

View full text Add to dashboard Cite

show abstract

“…The spacing (S) metric numerically describes the spread of the vectors in known PF [33,59]. This (18) and (19) define this metric:…”

Section: Spacing (S)mentioning

confidence: 99%

“…A hybrid charged system search and particle swarm multi-objective optimization is due to [33], where the answers space was divided based to some spaces in order to find a uniform Pareto points. A multi-objective charged system search has been developed by Kaveh and Massoudi[42], These algorithm have extended of the single objective charged system search (CSS), introduced by Kaveh and Talatahari [43,44].…”

Section: IImentioning

confidence: 99%

“…In 1985, Schaffer was probably the first to use Vector Evaluated Genetic Algorithms (VEGA) to solve multi-objective optimization, without using any composite aggregation and by combining all objectives into a single objective [18]. After that, a wide variety of MOEAs have been suggested, such as Micro-Genetic Algorithm (Micro-GA) [19] , Non-dominated Sorting Genetic Algorithm (NSGA) [20] , New variant of NSGA or NSGA-II [21] , Strength Pareto Evolutionary Algorithm (SPEA) [22] , SPEA2 [23] , Pareto Archive Evolution Strategy (PAES) [24] , Pareto Differential Evolution Approach (PDEA) [25], MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition [26], NSGAII based on Differential Evolution (NSGAII-DE [27], A hybrid multi-objective particle swarm optimization and decision making procedure for optimal design of truss structures [28], The third version of Generalized Differential Evolution (GDE3) [29], Multi-Objective Differential 4 Evolution-the Ranking-based Mutation Operator (MODE-RMO) [30], Multi-Objective Particle Swarm Optimization (MOPSO) [31], Differential Evolution for Multi-Objective Optimization (DEMO) [32], A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization [33], Multi-Objective Differential Evolution (MODE) [34], MultiObjective bees algorithms(Bees) [35] and Non-dominated Rank Genetic Algorithm (NRGA) [36]. Recent years, some other types of algorithms were also developed such as Multi-Objective Cuckoo Search (MOCS) [37], Multi-Objective Firefly Algorithm (MOFA) [38], A new multiswarm multi-objective optimization method for structural design [39], A swarm based memetic evolutionary algorithm for multi-objective optimization of large structures [40], Multi-Objective Flower Pollination Algorithms (MOFPA) [17] and Multi-objective Optimization Method Based on Sensitivity Analysis [14].…”

Section: )mentioning

confidence: 99%

See 1 more Smart Citation

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

2018

View full text Add to dashboard Cite

show abstract

“…The use of this algorithm is growing and its application is extending to various optimization problems [27][28][29][30][31][32][33][34][35]. A typical algorithm for the CSS is shown in Figure 1.…”

Section: Charged System Searchmentioning

confidence: 99%

Induction motor parameter estimation using metaheuristic methods

Çanakoğlu¹,

Yetgin²,

Temurtaş³

et al. 2014

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:The steady-state equivalent circuit parameters of an induction motor can be estimated using the operation characteristics that are provided by manufacturers. The characteristics of the motor used in estimation methods are the starting, maximum, and nominal torque values; the power factor; and efficiency. The operation characteristics of a motor given in data sheets are generally based on design parameters and are not suitable with real values. For this reason, in this paper, the data used in the parameter estimation for induction motors are taken from the literature.Using an optimization method for parameter estimation is useful for comparing the manufacturer values and values at the end of estimation, as well as minimizing the error in between. There are many methods in the literature for the parameter estimation of induction motors. In this study, the estimation is made using the charged system search (CSS), differential evolution algorithm (DEA), particle swarm optimization, and genetic algorithm optimization techniques. The CSS algorithm is first applied for estimation of the parameters of an induction motor. The results obtained from all of the methods show that the CSS algorithm is suitable with the DEA. From the obtained results, it is understood that an exact approach can be made to equivalent circuit parameters in case the values given by the manufacturer model the motor properly.

show abstract

A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization

Cited by 89 publications

References 21 publications

The application of water cycle algorithm to portfolio selection

The application of water cycle algorithm to portfolio selection

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

Induction motor parameter estimation using metaheuristic methods

Contact Info

Product

Resources

About