scholarly journals Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao-Xu Ma ◽  
Jie-Sheng Wang
Keyword(s):  
Optimization Problems ◽  
Bat Algorithm ◽  
Convergence Speed ◽  
Function Optimization ◽  
Test Functions ◽  
Simulation Experiments ◽  
Intelligent Optimization ◽  
Transmitted Pulse ◽  
Intelligent Optimization Algorithm ◽  
Searching Ability

The bat algorithm (BA) is a new bionic intelligent optimization algorithm to simulate the foraging behavior and the echolocation principle of the bats. The parameter initialization of the discussed binary bat algorithm (BBA) has important influence on the convergence speed, convergence precision, and good global searching ability of the BBA. The convergence speed and algorithm searching precision are determined by the pulse of loudness and pulse rate. The simulation experiments are carried out by using the six typical test functions to discuss this influence. The simulation results show that the convergence speed of the BBA is relatively sensitive to the setting of the algorithm parameters. The convergence precision reduces when increasing the rate of bat transmitted pulse alone and the convergence speed increases the launch loudness alone. The proper combination of BBA parameters (the rate of bat transmitted pulse and the launch loudness) can flexibly improve the algorithm’s convergence velocity and improve the accuracy of the searched solutions.

scholarly journals Improved Bat Algorithm Based on Multipopulation Strategy of Island Model for Solving Global Function Optimization Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Sha-Sha Guo ◽  
Jie-Sheng Wang ◽  
Xiao-Xu Ma
Keyword(s):  
Chaotic Map ◽  
Bat Algorithm ◽  
Convergence Speed ◽  
Function Optimization ◽  
Island Model ◽  
Search Performance ◽  
Simulation Experiments ◽  
Global Function ◽  
Contraction Factor ◽  
Improved Algorithm

The bat algorithm (BA) is a heuristic algorithm that globally optimizes by simulating the bat echolocation behavior. In order to improve the search performance and further improve the convergence speed and optimization precision of the bat algorithm, an improved algorithm based on chaotic map is introduced, and the improved bat algorithm of Levy flight search strategy and contraction factor is proposed. The optimal chaotic map operator is selected based on the simulation experiments results. Then, a multipopulation parallel bat algorithm based on the island model is proposed. Finally, the typical test functions are used to carry out the simulation experiments. The simulation results show that the proposed improved algorithm can effectively improve the convergence speed and optimization accuracy.

scholarly journals Flower Pollination Algorithm with Dimension by Dimension Improvement

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Rui Wang ◽  
Yongquan Zhou
Keyword(s):  
Optimization Problems ◽  
Convergence Speed ◽  
Function Optimization ◽  
Neighborhood Search ◽  
Flower Pollination Algorithm ◽  
Evaluation Strategy ◽  
Flower Pollination ◽  
Searching Ability ◽  
Improved Algorithm

Flower pollination algorithm (FPA) is a new nature-inspired intelligent algorithm which uses the whole update and evaluation strategy on solutions. For solving multidimension function optimization problems, this strategy may deteriorate the convergence speed and the quality of solution of algorithm due to interference phenomena among dimensions. To overcome this shortage, in this paper a dimension by dimension improvement based flower pollination algorithm is proposed. In the progress of iteration of improved algorithm, a dimension by dimension based update and evaluation strategy on solutions is used. And, in order to enhance the local searching ability, local neighborhood search strategy is also applied in this improved algorithm. The simulation experiments show that the proposed strategies can improve the convergence speed and the quality of solutions effectively.

Genetic Algorithm Based on Chaos Optimization

2010 ◽  
Vol 439-440 ◽  
pp. 641-645
Author(s):  
Chun Bo Xiu ◽  
Li Fen Lu ◽  
Yi Cheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Global Solutions ◽  
Function Optimization ◽  
Chaos Optimization ◽  
Fast Convergence Rate ◽  
Simulation Results ◽  
Searching Ability ◽  
Two Stages

A hybrid genetic algorithm is proposed based on chaos optimization. The optimization process can be divided into two stages every iteration, one is genetic coarse searching and the other is chaos elaborate searching. Genetic algorithm searches the global solutions in the origin space. An elaborate space near the center of superior individuals is divided from the origin space, which is searched by chaos optimization adequately to generate new better superior individuals for genetic operation. The elaborate space can be compressed quickly to accelerate searching rate and enhance the searching efficiency. In this way, the algorithm has global searching ability and fast convergence rate. The simulation results prove that the algorithm can give satisfied results to function optimization problems.

Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

2011 ◽  
Vol 148-149 ◽  
pp. 134-137 ◽  
Author(s):  
Pei Wei Tsai ◽  
Jeng Shyang Pan ◽  
Bin Yih Liao ◽  
Ming Jer Tsai ◽  
Vaci Istanda
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Computational Cost ◽  
Bat Algorithm ◽  
Experimental Results ◽  
Computational Time ◽  
Test Functions ◽  
Novel Algorithm

Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.

scholarly journals A Modified Whale Optimization Algorithm with Single-Dimensional Swimming for Global Optimization Problems

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1892
Author(s):  
Pengzhen Du ◽  
Weiming Cheng ◽  
Ning Liu ◽  
Haofeng Zhang ◽  
Jianfeng Lu
Keyword(s):  
Optimization Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Convergence Speed ◽  
Whale Optimization Algorithm ◽  
Well Performance ◽  
Simulation Experiments ◽  
Tent Map ◽  
Whale Optimization ◽  
Logarithm Function

As a novel meta-heuristic algorithm, the Whale Optimization Algorithm (WOA) has well performance in solving optimization problems. However, WOA usually tends to trap in local optimal and it suffers slow convergence speed for large-scale and high-dimension optimization problems. A modified whale optimization algorithm with single-dimensional swimming (abbreviated as SWWOA) is proposed in order to overcome the shortcoming. First, tent map is applied to generate the initialize population for maximize search ability. Second, quasi-opposition learning is adopted after every iteration for further improving the search ability. Third, a novel nonlinearly control parameter factor that is based on logarithm function is presented in order to balance exploration and exploitation. Additionally, the last, single-dimensional swimming is proposed in order to replace the prey behaviour in standard WOA for tuning. The simulation experiments were conducted on 20 well-known benchmark functions. The results show that the proposed SWWOA has better performance in solution precision and higher convergence speed than the comparison methods.

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.

Magnetotactic Bacteria Optimization Algorithm (MBOA) for Function Optimization

2018 ◽  
pp. 97-118
Author(s):  
Lili Liu ◽  
Hongwei Mo
Keyword(s):  
Optimization Algorithm ◽  
Development Process ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Magnetotactic Bacteria ◽  
Function Optimization ◽  
Experimental Results ◽  
Superior Performance ◽  
Test Functions ◽  
The Individual

Magnetotactic bacteria is a kind of prokaryotes with the characteristics of magnetotaxis. Magnetotactic bacteria optimization algorithm (MBOA) is an optimization algorithm based on the characteristics of magnetotaxis. It mimics the development process of magnetosomes (MTSs) in magnetotactic bacteria. In this chapter, four pairwise MTSs regulation schemes based on the best individual and randomly chosen one are proposed to study which scheme is more suitable for solving optimization problems. They are tested on 14 functions and compared with many popular optimization algorithms, including PSO, DE, ABC, and their variants. Experimental results show that all the schemes of MBOA are effective for solving most of test functions but have different performance on a few test functions. The fourth MBOA scheme has superior performance to the compared methods on many test functions. In this scheme, the algorithm searches around the current best individual to enhance the convergence of MBOA and the individual can migrate to the current best individual to enhance the diversity of the MBOA.

scholarly journals Multiscale Quantum Harmonic Oscillator Algorithm for Multimodal Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Wang ◽  
Kun Cheng ◽  
Yan Huang ◽  
Bo Li ◽  
Xinggui Ye ◽  
...  
Keyword(s):  
Harmonic Oscillator ◽  
Optimization Problems ◽  
Function Optimization ◽  
Local Optimum ◽  
Multimodal Optimization ◽  
Test Functions ◽  
Quantum Harmonic Oscillator ◽  
Multimodal Function Optimization ◽  
Multimodal Function ◽  
Benchmark Test Functions

This paper presents a variant of multiscale quantum harmonic oscillator algorithm for multimodal optimization named MQHOA-MMO. MQHOA-MMO has only two main iterative processes: quantum harmonic oscillator process and multiscale process. In the two iterations, MQHOA-MMO only does one thing: sampling according to the wave function at different scales. A set of benchmark test functions including some challenging functions are used to test the performance of MQHOA-MMO. Experimental results demonstrate good performance of MQHOA-MMO in solving multimodal function optimization problems. For the 12 test functions, all of the global peaks can be found without being trapped in a local optimum, and MQHOA-MMO converges within 10 iterations.

scholarly journals An Improved DV-Hop Localization Algorithm Based on Bat Algorithm

2016 ◽  
Vol 16 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Xiaoying Yang ◽  
Wanli Zhang
Keyword(s):  
Average Distance ◽  
Bat Algorithm ◽  
Localization Algorithm ◽  
Hop Count ◽  
Intelligent Optimization ◽  
Intelligent Optimization Algorithm ◽  
Anchor Nodes ◽  
Simulation Results ◽  
Unknown Node ◽  
Additional Hardware

Abstract In the DV-Hop algorithm, the average distance per hop is one of the factors that affect the accuracy of the positioning. In this paper, an improved DV-Hop localization algorithm based on bat algorithm (BAD-Hop) is proposed to solve the error which is brought by the average distance per hop. In BAD-Hop algorithm, bat algorithm which is a kind of intelligent optimization algorithm with good performance is introduced into DV-Hop localization algorithm to calculate average distance per hop of anchor nodes. Firstly, the average distance per hop of anchor node is calculated by using bat algorithm, which makes it closer to the actual value. Then the average distance per hop of the unknown node is weighted by using the average distance per hop of anchor nodes which hop-count is less than or equal to 3 to reduce errors caused by average distance per hop. Simulation results show that the improved algorithm can effectively reduce the positioning error without additional hardware.

A microbial treatment population dynamics optimization approach

2021 ◽  
pp. 1-15
Author(s):  
Jinding Gao
Keyword(s):  
Population Dynamics ◽  
Information Exchange ◽  
Information Diffusion ◽  
Optimization Problems ◽  
Microbial Control ◽  
Function Optimization ◽  
Optimization Approach ◽  
Dynamics Model ◽  
Population Dynamics Model ◽  
Number Of Individuals

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

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