Performance Enhancement of Differential Evolution by Incorporating Lévy Flight and Chaotic Sequence for the Cases of Satellite Images

2015 ◽  
Vol 6 (3) ◽  
pp. 69-81 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Md. Iqbal Quraishi ◽  
Sanjoy Das
Keyword(s):  
Differential Evolution ◽  
Satellite Image ◽  
Chaotic Sequence ◽  
Population Diversity ◽  
Lévy Flight ◽  
Step Size ◽  
Levy Flight ◽  
De Algorithm ◽  
Image Contrast Enhancement ◽  
Modified Algorithm

Differential Evolution (DE) is a simple but powerful evolutionary algorithm. Crossover Rate (CR) and Mutation Factor (F) are the most important control parameters in DE. Mutation factor controls the diversification. In traditional DE algorithm CR and F are kept constant. In this paper, the values of CR and F are modified to enhance the capability of traditional DE algorithm. In the first modified algorithm chaotic sequence is used to perform this modification. In the next modified algorithm Lévy Flight with chaotic step size is used for such enhancement. In the second modified DE, population diversity has been used to build population in every generation. As a result the algorithm does not converge prematurely. Both modified algorithms have been applied to optimize parameters of the parameterized contrast stretching function. The algorithms are tested for satellite image contrast enhancement and the results are compared, which show that DE via chaotic Lévy and population diversity information outperforms the traditional and chaotic DE in the image enhancement domain.

scholarly journals A Swarm Robotic Exploration Strategy Based on an Improved Random Walk Method

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Bao Pang ◽  
Yong Song ◽  
Chengjin Zhang ◽  
Hongling Wang ◽  
Runtao Yang
Keyword(s):  
Brownian Motion ◽  
Random Walk ◽  
Swarm Robotics ◽  
Search Strategy ◽  
Lévy Flight ◽  
Step Size ◽  
Levy Flight ◽  
Random Walk Method ◽  
Robotic Exploration ◽  
Exploration Mission

An environment can be searched far more efficiently if the appropriate search strategy is used. Because of the limited individual abilities of swarm robots, namely, local sensing and low processing power, random searching is the main search strategy used in swarm robotics. The random walk methods that are used most commonly are Brownian motion and Lévy flight, both of which mimic the self-organized behavior of social insects. However, both methods are somewhat limited when applied to swarm robotics, where having the robots search repeatedly can result in highly inefficient searching. Therefore, by analyzing the characteristics of swarm robotic exploration, this paper proposes an improved random walk method in which each robot adjusts its step size adaptively to reduce the number of repeated searches by estimating the density of robots in the environment. Simulation experiments and experiments with actual robots are conducted to study the effectiveness of the proposed method and evaluate its performance in an exploration mission. The experimental results presented in this paper show that an area is covered more efficiently using the proposed method than it is using either Brownian motion or Lévy flight.

scholarly journals Optimal Lévy-flight foraging in a finite landscape

2015 ◽  
Vol 12 (104) ◽  
pp. 20141158 ◽  
Author(s):  
Kun Zhao ◽  
Raja Jurdak ◽  
Jiajun Liu ◽  
David Westcott ◽  
Branislav Kusy ◽  
...  
Keyword(s):  
Power Law ◽  
Animal Movement ◽  
Foraging Strategy ◽  
Foraging Strategies ◽  
Foraging Efficiency ◽  
Lévy Flight ◽  
Step Size ◽  
Levy Flight ◽  
Ballistic Motion ◽  
Wide Range

We present a simple model to study Lévy-flight foraging with a power-law step-size distribution in a finite landscape with countable targets. We find that different optimal foraging strategies characterized by a wide range of power-law exponent μ opt , from ballistic motion ( μ opt → 1) to Lévy flight (1 < μ opt < 3) to Brownian motion ( μ opt ≥ 3), may arise in adaptation to the interplay between the termination of foraging, which is regulated by the number of foraging steps, and the environmental context of the landscape, namely the landscape size and number of targets. We further demonstrate that stochastic returning can be another significant factor that affects the foraging efficiency and optimality of foraging strategy. Our study provides a new perspective on Lévy-flight foraging, opens new avenues for investigating the interaction between foraging dynamics and the environment and offers a realistic framework for analysing animal movement patterns from empirical data.

Lèvy Flight Based Local Search in Differential Evolution

2013 ◽  
pp. 248-259 ◽  
Author(s):  
Harish Sharma ◽  
Shimpi Singh Jadon ◽  
Jagdish Chand Bansal ◽  
K. V. Arya
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Lévy Flight ◽  
Levy Flight

A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

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.

scholarly journals Synthesis of Sparse Concentric Ring Arrays Based on Fitness-Associated Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Xujian Wang ◽  
Minli Yao ◽  
Fenggan Zhang ◽  
Dingcheng Dai
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Concentric Ring ◽  
Exploration Process ◽  
De Algorithm ◽  
Crossover Probability ◽  
Negative Effect ◽  
Evolution Algorithm ◽  
Enrich Population

In this paper, fitness-associated differential evolution (FITDE) algorithm is proposed and applied to the synthesis of sparse concentric ring arrays under constraint conditions, whose goal is to reduce peak sidelobe level. In unmodified differential evolution (DE) algorithm, crossover probability is constant and remains unchanged during the whole optimization process, resulting in the negative effect on the population diversity and convergence speed. Therefore, FITDE is proposed where crossover probability can change according to certain information. Firstly, the population fitness variance is introduced to the traditional differential evolution algorithm to adjust the constant crossover probability dynamically. The fitness variance in the earlier iterations is relatively large. Under this circumstance, the corresponding crossover probability shall be small to speed up the exploration process. As the iteration progresses, the fitness variance becomes small on the whole and the crossover probability should be set large to enrich population diversity. Thereby, we construct three variation strategies of crossover probability according to the above changing trend. Secondly, FITDE is tested on benchmark functions, and the best one of the three strategies is determined according to the test results. Finally, sparse concentric ring arrays are optimized using FITDE, of which the results are compared with reference algorithms. The optimization results manifest the advantageous effectiveness of FITDE.

scholarly journals Levy Flight and Chaos Theory Based Gravitational Search Algorithm for Global Optimization

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Performance Metrics ◽  
Chaotic Maps ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Lévy Flight ◽  
Step Size ◽  
Levy Flight ◽  
Statistical Measures ◽  
Engineering Design Problems ◽  
Gravitational Search

The Gravitational Search Algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on well-known 23 classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has also been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.

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