scholarly journals SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput

2019 ◽  
Vol 2019 ◽  
pp. 1-18
Author(s):  
Hongbo Wang ◽  
Xiaoxiao Zhen ◽  
Xuyan Tu
Keyword(s):  
Cognitive Radio ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Population Diversity ◽  
Experimental Simulation ◽  
Target Space ◽  
Throughput Optimization ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping ◽  
Complex Target

Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. This proposed method hikes its local searching for a certain number of subgroups, and their individuals join together and share their mutual information among different subgroups, which improves the population diversity and achieves the purpose of fast global search during the whole Differential Evolution. The SFDE is examined by 20 well-known numerical benchmark functions, and those obtained results are compared with four other related algorithms. The experimental simulation in solving the problem of effective throughput optimization for cognitive users shows that the proposed SFDE is effective.

Performance Studies on Differential Evolution Algorithm and Shuffled Frog-Leaping Algorithm for Simulated Manufacturing Problems

2016 ◽  
Vol 835 ◽  
pp. 858-863 ◽  
Author(s):  
Lakkana Ruekkasaem ◽  
Pasura Aungkulanon
Keyword(s):  
Differential Evolution ◽  
Performance Studies ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Differential Evolution Algorithm ◽  
Shuffled Frog Leaping Algorithm ◽  
Linear Optimization Problems ◽  
Shuffled Frog Leaping ◽  
Manufacturing Problems ◽  
Maximum Minimum

The real world engineering problems are complex associated with lot of factors. The objective of mathematic models in simulated manufacturing problems are to minimize cost or maximize profits while satisfying the constraints. The purpose of this article was to study two algorithms for testing their efficiency in solving non-linear optimization problems and simulated manufacturing problems. A well-known meta-heuristic approach called Differential Evolution (DE) was compared with Shuffled Frog-leaping Algorithm (SFLA) in term of mean, maximum, minimum, and standard deviation of the solution. SFLA was better than DE in terms of the performance to finding optimal solutions because of the unique process of memeplex, which can increase speed of convergence and find turning parameters.

Enhanced Shuffled Frog Leaping Algorithm with Modified Memeplexes

2020 ◽  
Vol 10 ◽  
Author(s):  
Tarun Kumar Sharma ◽  
Ajith Abraham ◽  
Jitendra Rajpurohit
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Standard Test ◽  
Shuffled Frog Leaping Algorithm ◽  
Local Optima ◽  
Acceleration Rate ◽  
New Variant ◽  
Shuffled Frog Leaping ◽  
Level Of Significance ◽  
Better Than

Aims: To design a new variant of Shuffled Frog Leaping Algorithm in which memeplexes formation is modified with new strategy. Background: Shuffled frog leaping (SFL) is a memetic meta-heuristic algorithm that inherits the features of two other algorithms. Its intensification component of search is similar to Particle Swarm Optimization while the inspiration for diversification is inherited from the global exchange of information in Shuffled Complex Evolution. Basic variant has been applied to solve many optimisation problems. SFLA suffers with slow acceleration rate. Objective: To propose a robust hybrid SFLA that accelerates convergence. Method: Two modifications are proposed in the structure of basic SFLA. Firstly, memeplexes formation is modified to handle continuous optimization problems. Secondly, in basic SFL algorithm the position of worst frog is improved by moving it towards the best frog in the respective memeplex, with the progress of execution, the difference between best and worst frog position reduces; there may be more chances to trap in local minima. With an aim to improve convergence and avoiding trapping in local optima a parent centric operator is embedded in each memeplex while performing a local search. The proposed algorithm is named as PC-SFLA (Parent Centric - Shuffled frog leaping algorithm) Result: The improved efficiency of PC-SFLA is validated on a robust and diverse set of standard test functions defined in CEC 2006 and 2010 and further its efficacy is verified to optimize the total cost of Supply chain management of a system. Non-parametric statistical result analysis demonstrates the efficiency of the proposal. Conclusion: PC-SFLA performed better than PSO, DE, PESO+, Modified DE, ABC and SFLA at 5% and 10% level of significance where as at par with Shuffled-ABC for g01-g07 functions of CEC 2006 in terms of NFE’s. Similarly, PCSFLA performed better than SaDE, SFLA, CMODE at both level of significance (5% & 10%) and at par with MPDE in terms of mean function value for 17 problems taken from CEC 2006. Further PC-SFLA is investigated on a set of 18 problems from CEC 2010 and Wilcoxon signed ranks test is performed at 5% level of significance. PC-SFLA performed better than SFLA and CHDE and at par with PESO. The computational results present the competency of the proposed method to solve quadratic, nonlinear, polynomial, linear as well as cubic functions efficiently. The simulated results shows that the proposed algorithm is capable of solving mix integer constrained continuous optimization problem efficiently.

Multi-objective parameter optimization of the HYPE model using shuffled frog-leaping algorithm

2021 ◽  
Author(s):  
Xinyu Li ◽  
Prajna Kasargodu Anebgailu ◽  
Jörg Dietrich
Keyword(s):  
Optimization Problems ◽  
Population Based ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Hydrological Models ◽  
Watershed Model ◽  
Shuffled Frog Leaping Algorithm ◽  
Multi Objective ◽  
Combinatorial Optimization Problems ◽  
Shuffled Frog Leaping

<p>The calibration of hydrological models using bio-inspired meta-heuristic optimization techniques has been extensively tested to find the optimal parameters for hydrological models. Shuffled frog-leaping algorithm (SFLA) is a population-based cooperative search technique containing virtual interactive frogs distributed into multiple memeplexes. The frogs search locally in each memeplex and are periodically shuffled into new memeplexes to ensure global exploration. Though it is developed for discrete optimization, it can be used to solve multi-objective combinatorial optimization problems as well.</p><p>In this study, a hydrological catchment model, Hydrological Predictions for the Environment (HYPE) is calibrated for streamflow and nitrate concentration in the catchment using SFLA. HYPE is a semi-distributed watershed model that simulates runoff and other hydrological processes based on physical as well as conceptual laws. SFLA with 200 runtimes and 5 memeplexes containing 10 frogs each is used to calibrate 22 model parameters. It is compared with manual calibration and Differential Evolution Markov Chain (DEMC) method from the HYPE-tool. The preliminary results of the statistical performance measures for streamflow calibration show that SFLA has the fastest convergence speed and higher stability when compared with the DEMC method. NSE of 0.68 and PBIAS of 7.72 are recorded for the best run of SFLA during the calibration of streamflow. In comparison, the HYPE-tool DEMC produced the best NSE of 0.45 and a PBIAS of -3.37 while the manual calibration resulted in NSE of 0.64 and PBIAS of 2.01.</p>

Sign in / Sign up

Export Citation Format

Share Document