Self organizing migrating algorithm with quadratic interpolation for solving large scale global optimization problems

Singh, Dipti; Agrawal, Shashank

doi:10.1016/j.asoc.2015.09.033

Cited by 33 publications

(7 citation statements)

References 5 publications

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“…It also introduced adaptive control parameters for each migration loop, the performance has been demonstrated through the CEC'13 and CEC'17 benchmark test suites. In addition, other variants of SOMA have also confirmed its superior performance compared to the original version such as SOMGA [10], C-SOMGA [9], CSOMA [28], SOMAQI [30], M-SOMAQI [29], mNM-SOMA [1], and HSOMA [24].…”

Section: Introductionmentioning

confidence: 81%

Self-Organizing Migrating Algorithm Pareto

Diep

Zelinka

Das

2019

mendel

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In this paper, we propose a new method named Pareto-based self-organizing migrating algorithm (SOMA Pareto), in which the algorithm is divided into the Organization, Migration, and Update processes. The important key in the Organization process is the application of the Pareto Principle to select the Migrant and the Leader, increasing the performance of the algorithm. The adaptive PRT, Step, and PRTVector parameters are applied to enhance the ability to search for promising subspaces and then to focus on exploiting that subspaces. Based on the testing results on the well-known benchmark suites such as CEC'13, CEC'15, and CEC'17, the superior performance of the proposed algorithm compared to the SOMA family and the state-of-the-art algorithms such as variant DE and PSO are confirmed. These results demonstrate that SOMA Pareto is an effective, promising algorithm.

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Section: Introductionmentioning

confidence: 81%

Self-Organizing Migrating Algorithm Pareto

Diep

Zelinka

Das

2019

mendel

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“…In order to further improve the global search ability of WOA and solve large-scale global optimization problems, a QI method is used in WOA to improve the search ability of algorithm. In recent years, QI has been widely used in meta-heuristic optimization algorithms (Singh and Agrawal, 2016;Gupta et al, 2017). QI uses three vectors selected in an n-dimensional space to find the minimum value on a quadratic curve, called the second crossing.…”

Section: Improvement Based On Qimentioning

confidence: 99%

Model for Predicting NO<sub><i>x</i></sub> Emission from Boilers Based on MWOA-LSSVM Integration

Zhen

Liu

2019

J. Chem. Eng. Japan / JCEJ

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Accurate, reliable prediction of NO x emission in ue gas is of great signi cance for operation of power station boilers with low nitrogen emissions. To improve the accuracy of a prediction model, a method for predicting NO x emission from boilers based on integration of the whale optimization algorithm and least squares support vector machine (MWOA-LSSVM) is proposed in this paper. First, the sample space is divided, and a segmentation logistic chaotic map is then used to initialize the population. The nonlinear adaptive parameters are improved, and quadratic interpolation update position is used to improve the whale optimization algorithm (WOA) by broadening the global exploration ability of the algorithm. The MWOA is used to globally optimize the kernel function width and penalty factor of the LSSVM submodel in each subspace, yielding the sub-model as an output. Finally, the sub-model output is integrated using the least squares regression, yielding the output from the integrated model. The simulation results show that the MWOA-LSSVM integrated model has stable, high-precision simulation performance compared with other selected prediction models and can provide more accurate predictions of NO x emissions from boilers.

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“…The initial positions of all CBs are determined randomly in an m-dimensional search space according to equation (5). Where x i 0 is the initial solution vector of the ith CB.…”

Section: Enhanced Cbomentioning

confidence: 99%

“…As a result, a memetic artificial bee colony (MABC) algorithm has been developed to solve large-scale global optimization problems. Self-organizing migrating algorithm with quadratic interpolation (SOMAQI) has been extended by Singh and Agrawal 5 to solve large-scale global optimization problems for dimensions ranging from 100 to 3000 with a constant population size of 10 only. This produces high-quality optimal solutions with very low computational cost and converges very fast to optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of nonlinear large-scale suspendome using cascade optimization

Kaveh

Rezaei

Shiravand

2017

International Journal of Space Structures

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Large-scale suspendomes are elegant architectural structures which cover a vast area with no interrupting columns in the middle. These domes have attractive shapes which are also economical. Domes are built in a wide variety of forms. In this article, an algorithm is developed for optimum design of domes considering the topology, geometry, and size of member section using the cascade-enhanced colliding bodies optimization method. In large-scale space steel structures, a large number of design variables are involved. The idea of cascade optimization allows a single optimization problem to be tackled in a number of successive autonomous optimization stages. The variables are the optimum height of crown and tubular sections of these domes, the initial strain, the length of the struts, and the cross-sectional areas of the cables in the tensegrity system of domes. The number of joints in each ring and the number of rings are considered for topology optimization of ribbed and Schwedler domes. Weight of the dome is taken as the objective function for minimization. A simple procedure is defined to determine the configuration of the domes. The design constraints are considered according to the provisions of Load and Resistance Factor Design-American Institute of Steel Constitution. In order to investigate the efficiency of the presented method, a large-scale suspendome with more than 2266 members is investigated. Numerical results show that the utilized method is an efficient tool for optimal design of large-scale domes. Additionally, in this article, a topology and geometry optimization for two common ribbed and Schwedler domes are performed to find their optimum graphs considering various spans.

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Self organizing migrating algorithm with quadratic interpolation for solving large scale global optimization problems

Cited by 33 publications

References 5 publications

Self-Organizing Migrating Algorithm Pareto

Self-Organizing Migrating Algorithm Pareto

Model for Predicting NO<sub><i>x</i></sub> Emission from Boilers Based on MWOA-LSSVM Integration

Optimal design of nonlinear large-scale suspendome using cascade optimization

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