A novel Invasive Weed Optimization with levy flight for optimization problems: The case of forecasting energy demand

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.

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Solving Nonlinear Constrained Optimization Problems Using Invasive Weed Optimization

Advances in Intelligent Systems and Computing - Intelligent Computing, Communication and Devices ◽

10.1007/978-81-322-2012-1_14 ◽

2014 ◽

pp. 127-133 ◽

Cited By ~ 2

Author(s):

Y. Ramu Naidu ◽

A.K. Ojha

Keyword(s):

Constrained Optimization ◽

Optimization Problems ◽

Invasive Weed Optimization ◽

Constrained Optimization Problems ◽

Invasive Weed ◽

Nonlinear Constrained Optimization

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A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

International Journal of Distributed Systems and Technologies ◽

10.4018/ijdst.2021010104 ◽

2021 ◽

Vol 12 (1) ◽

pp. 49-66

Author(s):

Yu Li ◽

Yiran Zhao ◽

Jingsen Liu

Keyword(s):

Real World ◽

Optimization Problems ◽

Population Diversity ◽

Lévy Flight ◽

Simulation Experiments ◽

Mathematical Functions ◽

Levy Flight ◽

Engineering Problems ◽

Sine Cosine Algorithm ◽

Swarm Intelligence Algorithm

The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.

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Insulin DNA Sequence Classification Using Levy Flight Bat With Back Propagation Algorithm

Advances in Medical Technologies and Clinical Practice - Mobile Devices and Smart Gadgets in Medical Sciences ◽

10.4018/978-1-7998-2521-0.ch011 ◽

2020 ◽

pp. 232-252

Author(s):

Siyab Khan ◽

Abdullah Khan ◽

Rehan Ullah ◽

Maria Ali ◽

Rahat Ullah

Keyword(s):

Dna Sequence ◽

Dna Sequences ◽

Optimization Problems ◽

Back Propagation ◽

Bat Algorithm ◽

Hybrid Models ◽

Lévy Flight ◽

Healthy Human ◽

Levy Flight ◽

Nature Inspired Algorithms

Various nature-inspired algorithms are used for optimization problems. Recently, one of the nature-inspired algorithms became famous because of its optimality. In order to solve the problem of low accuracy, famous computational methods like machine learning used levy flight Bat algorithm for the problematic classification of an insulin DNA sequence of a healthy human, one variant of the insulin DNA sequence is used. The DNA sequence is collected from NCBI. Preprocessing alignment is performed in order to obtain the finest optimal DNA sequence with a greater number of matches between base pairs of DNA sequences. Further, binaries of the DNA sequence are made for the aim of machine readability. Six hybrid algorithms are used for the classification to check the performance of these proposed hybrid models. The performance of the proposed models is compared with the other algorithms like BatANN, BatBP, BatGDANN, and BatGDBP in term of MSE and accuracy. From the simulations results it is shown that the proposed LFBatANN and LFBatBP algorithms perform better compared to other hybrid models.

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Lévy flight trajectory-based whale optimization algorithm for engineering optimization

Engineering Computations ◽

10.1108/ec-07-2017-0264 ◽

2018 ◽

Vol 35 (7) ◽

pp. 2406-2428 ◽

Cited By ~ 13

Author(s):

Yongquan Zhou ◽

Ying Ling ◽

Qifang Luo

Keyword(s):

Optimization Problems ◽

Engineering Optimization ◽

Lévy Flight ◽

Flight Trajectory ◽

Levy Flight ◽

Content Type ◽

Search Spaces ◽

Engineering Design Problems ◽

Whale Optimization ◽

Engineering Optimization Problems

Purpose This paper aims to represent an improved whale optimization algorithm (WOA) based on a Lévy flight trajectory and called the LWOA algorithm to solve engineering optimization problems. The LWOA makes the WOA faster, more robust and significantly enhances the WOA. In the LWOA, the Lévy flight trajectory enhances the capability of jumping out of the local optima and is helpful for smoothly balancing exploration and exploitation of the WOA. It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions. Design/methodology/approach In this paper, an improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is represented to solve engineering optimization problems. Findings It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions. Originality value An improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is first proposed.

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A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems

Engineering With Computers ◽

10.1007/s00366-020-00994-0 ◽

2020 ◽

Cited By ~ 5

Author(s):

Saeid Barshandeh ◽

Maryam Haghzadeh

Keyword(s):

Optimization Problems ◽

Lévy Flight ◽

Levy Flight ◽

Search Optimization ◽

Tree Seed

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The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems

The Scientific World JOURNAL ◽

10.1155/2014/831691 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 13

Author(s):

Henryk Josiński ◽

Daniel Kostrzewa ◽

Agnieszka Michalczuk ◽

Adam Świtoński

Keyword(s):

Optimization Problems ◽

Space Exploration ◽

Optimization Methods ◽

Search Space ◽

Invasive Weed Optimization ◽

Invasive Weed ◽

Hybrid Strategy ◽

Mona Lisa ◽

The Traveling Salesman Problem ◽

Discrete Optimization Problems

This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. The achieved results are compared with analogous outcomes produced by other optimization methods reported in the literature.

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

International Journal of Antennas and Propagation ◽

10.1155/2014/630529 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 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.

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