scholarly journals Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Yanyan Tan ◽  
Xue Lu ◽  
Yan Liu ◽  
Qiang Wang ◽  
Huaxiang Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multiobjective Optimization Problems ◽  
Distinct Features

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

A Novel Multiobjective Memetic Algorithm Based on IWO-DE and its Application in Nutrition Decision Making Problem

2014 ◽  
Vol 989-994 ◽  
pp. 1849-1852
Author(s):  
Gao Ping Wang ◽  
Meng Zhang ◽  
Wei Wei Zhao
Keyword(s):  
Decision Making ◽  
Multiobjective Optimization ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Memetic Algorithms ◽  
Pareto Optimum ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multiobjective Optimization Problems ◽  
Decision Making Problem

In this paper ,we discuss multiobjective optimization problems solved by Memetic algorithms. We present A novel multiobjective memetic algorithm based on invasive weed optimization and differential evolution (IWO-DE) to solve this class of problems .We present the Nutrition Prescription Model for Meals.the IWO-DE is applied to solve the nutrition decision making problem to map the Pareto-optimum front. The results in the problem show its effectiveness.

scholarly journals Synthesis of Phase-Only Reconfigurable Linear Arrays Using Multiobjective Invasive Weed Optimization Based on Decomposition

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Liu ◽  
Yong-Chang Jiao ◽  
Ya-Ming Zhang ◽  
Yan-Yan Tan
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Amplitude Distribution ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Linear Arrays ◽  
Reconfigurable Array ◽  
Multiobjective Optimization Problems ◽  
Antenna Array Synthesis

Synthesis of phase-only reconfigurable array aims at finding a common amplitude distribution and different phase distributions for the array to form different patterns. In this paper, the synthesis problem is formulated as a multiobjective optimization problem and solved by a new proposed algorithm MOEA/D-IWO. First, novel strategies are introduced in invasive weed optimization (IWO) to make original IWO fit for solving multiobjective optimization problems; then, the modified IWO is integrated into the framework of the recently well proved competitive multiobjective optimization algorithm MOEA/D to form a new competitive MOEA/D-IWO algorithm. At last, two sets of experiments are carried out to illustrate the effectiveness of MOEA/D-IWO. In addition, MOEA/D-IWO is compared with MOEA/D-DE, a new version of MOEA/D. The comparing results show the superiority of MOEA/D-IWO and indicate its potential for solving the antenna array synthesis problems.

scholarly journals Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller

2019 ◽  
Vol 6 (3) ◽  
pp. 284-295 ◽  
Author(s):  
Mojgan Misaghi ◽  
Mahdi Yaghoobi
Keyword(s):  
Standard Deviation ◽  
Chaos Theory ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Optimization Problems ◽  
Optimization Method ◽  
Population Based ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Chaotic Mapping

Abstract Weed is a phenomenon which is looks for optimality and finds the best environment for life and quickly adapts itself to environmental conditions and resists changes. Considering these features, a powerful optimization algorithm is developed in this study. The invasive weed optimization algorithm (IWO) is a population-based evolutionary optimization method inspired by the behavior of weed colonies. In this paper, the IWO algorithm is based on chaos theory. Among parameters of weed optimization algorithm, standard deviation affects the performance of the algorithm significantly. Therefore, chaotic maps are used in the standard deviation parameter. Performance of the chaotic invasive weed development method is investigated on five benchmark functions, using logistic chaotic mapping. Additionally, the problem of setting the PID controller parameters for a DC motor using the proposed method is discussed. The statistical results on optimization problems show that the improved chaotic weed algorithm has gained fast convergence rate and high accuracy. Highlights Improved Invasive weed optimization Algorithm (IWO) based on Chaos theory. Improved setting the parameters of PID controller uses Chaotic IWO Algorithm.

scholarly journals A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ding Han ◽  
Jianrong Zheng
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Kriging Model ◽  
Utilization Efficiency ◽  
Function Evaluation ◽  
Good Convergence ◽  
Model Based ◽  
Multiobjective Optimization Problems ◽  
R2 Indicator

Most of the multiobjective optimization problems in engineering involve the evaluation of expensive objectives and constraint functions, for which an approximate model-based multiobjective optimization algorithm is usually employed, but requires a large amount of function evaluation. Aiming at effectively reducing the computation cost, a novel infilling point criterion EIR2 is proposed, whose basic idea is mapping a point in objective space into a set in expectation improvement space and utilizing the R2 indicator of the set to quantify the fitness of the point being selected as an infilling point. This criterion has an analytic form regardless of the number of objectives and demands lower calculation resources. Combining the Kriging model, optimal Latin hypercube sampling, and particle swarm optimization, an algorithm, EIR2-MOEA, is developed for solving expensive multiobjective optimization problems and applied to three sets of standard test functions of varying difficulty and comparing with two other competitive infill point criteria. Results show that EIR2 has higher resource utilization efficiency, and the resulting nondominated solution set possesses good convergence and diversity. By coupling with the average probability of feasibility, the EIR2 criterion is capable of dealing with expensive constrained multiobjective optimization problems and its efficiency is successfully validated in the optimal design of energy storage flywheel.

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