A modified competitive swarm optimizer for large scale optimization problems

Mohapatra, Prabhujit; Das, Kedar Nath; Roy, Santanu

doi:10.1016/j.asoc.2017.05.060

Cited by 122 publications

(48 citation statements)

References 37 publications

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“…Despite the noticeable promising implementation of the ELM model in wide range of hydrology applications, it still suffers limitations and drawbacks. First, the random assignment of the input weights and biases can negatively affect the generalization capability of the network [30]. In addition, ELM needs larger number of hidden neurons which results in more complex models [31].…”

Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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e capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. e classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. is current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. e results are evaluated based on several statistical measures and graphical presentations. e river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

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Section: Introductionmentioning

confidence: 99%

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Faris

Al‐Ansari

2020

Complexity

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“…Although the original PSO fails on large-scale optimization problems due to the increasing roaming behavior (i.e., the tendency of particles to leave the search space early in the search process) in high-dimensional space [12], some adaptations of PSO [17], [18], [46] as well as CSO [45], [47] have been shown to be promising in tackling large-scale SOPs. Nevertheless, existing multi-objective PSO and CSO algorithms are effective in solving MOPs with no more than 50 decision variables x l (1) x l (2) x w x l (3) Fig.…”

Section: B Discussionmentioning

confidence: 99%

Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer

Tian

Zheng

Zhang

et al. 2020

IEEE Trans. Cybern.

255

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There exist many multi-objective optimization problems (MOPs) containing a large number of decision variables in real-world applications, which are known as large-scale MOPs. Due to the ineffectiveness of existing operators in finding optimal solutions in a huge decision space, some decision variable division based algorithms have been tailored for improving the search efficiency in solving large-scale MOPs. However, these algorithms will encounter difficulties when solving problems with complicated landscapes, as the decision variable division is likely to be inaccurate and time-consuming. In this paper, we propose a competitive swarm optimizer (CSO) based efficient search for solving large-scale MOPs. The proposed algorithm adopts a new particle updating strategy that suggests a twostage strategy to update position, which can highly improve the search efficiency. Experimental results on large-scale benchmark MOPs and an application example demonstrate the superiority of the proposed algorithm over several stateof-the-art multi-objective evolutionary algorithms, including problem transformation based algorithm, decision variable clustering based algorithm, particle swarm optimization algorithm, and estimation of distribution algorithm. Index Terms-Evolutionary multi-objective optimization, large-scale multi-objective optimization problem, competitive swarm optimizer, particle swarm optimization I. INTRODUCTION M ULTI-objective optimization problems (MOPs) are commonly seen in real-world applications [1]-[5], which are characterized by multiple objectives conflicting with each other. Due to the conflicting nature of the objectives, a set of trade-off solutions rather than a single Manuscript received- .

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“…In another work, Mohapatra et al introduced a modified competitive swarm optimizer (CSO) for solving the large-scale optimization problems. 25 In this method, unlike the CSO, two-thirds of the populations are updated by the tri-competitive criterion. The main point of this method is to maintain higher speed of discovery in the search space with a higher convergence rate.…”

Section: Related Workmentioning

confidence: 99%

A multi‐agent system based for solving high‐dimensional optimization problems: A case study on email spam detection

Mohammadzadeh

Gharehchopogh

2020

Int J Communication

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Summary There exist numerous high‐dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high‐dimensional problems. An appropriate approach to solve such problems is to apply multi‐agent systems (MASs) along with the metaheuristic algorithms. The present paper proposes a new approach based on the MASs and the concept of agent, which is named MAS as Metaheuristic (MAMH) method. In the proposed method, several basic and powerful metaheuristic algorithms are considered as separate agents, each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. Altogether, the proposed method was tested on 32 complex benchmark functions, the results of which indicated the effectiveness and powerfulness of the proposed method for solving high‐dimensional optimization problems. In addition, in this paper, the binary version of the proposed method, called Binary MAMH (BMAMH), was implemented on the email spam detection. According to the results, the proposed method exhibited a higher degree of precision in the detection of spam emails compared to other metaheuristic algorithms and methods.

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A modified competitive swarm optimizer for large scale optimization problems

Cited by 122 publications

References 37 publications

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer

A multi‐agent system based for solving high‐dimensional optimization problems: A case study on email spam detection

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