Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

2015 ◽  
Vol 29 (5) ◽  
pp. 1133-1153 ◽  
Author(s):  
Chao Liu ◽  
Peifeng Niu ◽  
Guoqiang Li ◽  
Yunpeng Ma ◽  
Weiping Zhang ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Function Optimization ◽  
Shuffled Frog Leaping Algorithm ◽  
Numerical Function ◽  
Numerical Function Optimization ◽  
Shuffled Frog Leaping

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.

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

scholarly journals Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Xingwang Huang ◽  
Chaopeng Li ◽  
Yunming Pu ◽  
Bingyan He
Keyword(s):  
Probability Distribution ◽  
Bat Algorithm ◽  
Random Coefficients ◽  
Function Optimization ◽  
Premature Convergence ◽  
Local Optimum ◽  
Search Range ◽  
Numerical Function ◽  
Numerical Function Optimization ◽  
Gaussian Probability Distribution

Quantum-behaved bat algorithm with mean best position directed (QMBA) is a novel variant of bat algorithm (BA) with good performance. However, the QMBA algorithm generates all stochastic coefficients with uniform probability distribution, which can only provide a relatively small search range, so it still faces a certain degree of premature convergence. In order to help bats escape from the local optimum, this article proposes a novel Gaussian quantum bat algorithm with mean best position directed (GQMBA), which applies Gaussian probability distribution to generate random number sequences. Applying Gaussian distribution instead of uniform distribution to generate random coefficients in GQMBA is an effective technique to promote the performance in avoiding premature convergence. In this article, the combination of QMBA and Gaussian probability distribution is applied to solve the numerical function optimization problem. Nineteen benchmark functions are employed and compared with other algorithms to evaluate the accuracy and performance of GQMBA. The experimental results show that, in most cases, the proposed GQMBA algorithm can provide better search performance.

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