scholarly journals Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Haorui Liu ◽  
Fengyan Yi ◽  
Heli Yang
Keyword(s):  
Quantitative Research ◽  
Optimization Problems ◽  
Cloud Model ◽  
Continuous Optimization ◽  
Convergence Speed ◽  
Function Optimization ◽  
Local Optimum ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping ◽  
Continuous Optimization Problems

The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion.

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.

The Bees Algorithm: Modelling foraging behaviour to solve continuous optimization problems

2009 ◽  
Vol 223 (12) ◽  
pp. 2919-2938 ◽  
Author(s):  
D T Pham ◽  
M Castellani
Keyword(s):  
Foraging Behaviour ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Experimental Tests ◽  
Function Optimization ◽  
Bees Algorithm ◽  
Biologically Inspired ◽  
Neighbourhood Search ◽  
Continuous Optimization Problems ◽  
Almost All

The Bees Algorithm models the foraging behaviour of honeybees in order to solve optimization problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This article describes the Bees Algorithm in its basic formulation, and two recently introduced procedures that increase the speed and accuracy of the search. A critical review of the related swarm intelligence literature is presented. The effectiveness of the proposed method is compared to that of three state-of-the-art biologically inspired search methods. The four algorithms were tested on a range of well-known benchmark function optimization problems of different degrees of complexity. The experimental results proved the reliability of the bees foraging metaphor. The Bees Algorithm performed optimally, or near optimally, in almost all the tests. Compared to the three control algorithms, the Bees Algorithm was highly competitive in terms of learning accuracy and speed. The experimental tests helped also to shed further light on the search mechanisms of the Bees Algorithm and the three control methods, and to highlight their differences, strengths, and weaknesses.

A Novel Hybrid Optimization Method of Shuffled Frog Leaping Algorithm and Particle Swarm Optimization

2013 ◽  
Vol 717 ◽  
pp. 433-438 ◽  
Author(s):  
Mei Jin Lin ◽  
Fei Luo ◽  
Yu Ge Xu ◽  
Long Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Search Strategy ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Shuffled Frog Leaping

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization (PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, wPSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

An Efficient Hybrid Algorithm Based on HS and SFLA

2016 ◽  
Vol 30 (05) ◽  
pp. 1659012 ◽  
Author(s):  
Aijia Ouyang ◽  
Xuyu Peng ◽  
Yanbin Liu ◽  
Lilue Fan ◽  
Kenli Li
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Harmony Search ◽  
Complex Function ◽  
Continuous Optimization ◽  
Function Optimization ◽  
Local Optima ◽  
Hybrid Mechanism ◽  
Complex Functions ◽  
Continuous Optimization Problems

When used for optimizing complex functions, harmony search (HS) and shuffled frog leaping algorithm (SFLA) algorithm tend to easily get trapped into local optima and result in low convergence precision. To overcome such shortcomings, a hybrid mechanism of selective search by combining HS algorithm and SFLA algorithm is as well proposed. An HS-SFLA algorithm is designed by taking the advantages of HS and SFLA algorithms. The hybrid algorithm of HS-SFLA is adopted for dealing with complex function optimization problems, the experimental results show that HS-SFLA outperforms other state-of-the-art intelligence algorithms significantly in terms of global search ability, convergence speed and robustness on 80% of the benchmark functions tested. The HS-SFLA algorithm could directly be applied to all kinds of continuous optimization problems in the real world.

scholarly journals A Novel Tournament Selection Based Differential Evolution Variant for Continuous Optimization Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Qamar Abbas ◽  
Jamil Ahmad ◽  
Hajira Jabeen
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Convergence Speed ◽  
Local Optima ◽  
De Algorithm ◽  
Parent Selection ◽  
Tournament Selection ◽  
Overall Performance ◽  
Continuous Optimization Problems

Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in the recent years. A number of variants have been established for the algorithm that makes DE more applicable. However, most of the variants are suffering from the problems of convergence speed and local optima. A novel tournament based parent selection variant of DE algorithm is proposed in this research. The proposed variant enhances searching capability and improves convergence speed of DE algorithm. This paper also presents a novel statistical comparison of existing DE mutation variants which categorizes these variants in terms of their overall performance. Experimental results show that the proposed DE variant has significance performance over other DE mutation variants.

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