Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

Tsai, Pei‐Wei; Pan, Jie; Liao, Bin; Tsai, Ming Jer; Istanda, Vaci

doi:10.4028/www.scientific.net/amm.148-149.134

Cited by 113 publications

(71 citation statements)

References 7 publications

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“…A novel algorithm that can be applied in SDN for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm, which is called evolved bat algorithm (EBA). EBA is a new method in the branch of swarm intelligence for solving SDN network-scheduling problems [12]. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, the author redefines the corresponding operation to the bats' behaviors.…”

Section: Related Workmentioning

confidence: 99%

A bat algorithm for SDN network scheduling

Lin

2018

J Wireless Com Network

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With the development of SDN, end-to-end network reservation becomes a reality. Resource reservation can become an online service for network users, and the network faces new challenges on how to allocate users' resource dynamically. To solve the problem, this paper proposed a bat-based algorithm for SDN network scheduling along with a network request model. The network resource request model is for characterizing users' simultaneous network resource request in SDN. Based on the model, this paper transforms the resource reservation problem into a multiple-knapsack problem and proposes a bat algorithm to optimize the solution. Experiments show that the proposed algorithm is better than greedy and dynamic programming algorithm. The major contribution of this paper is to model the SDN users' resource requests and apply bat algorithm for the solution.

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Section: Related Workmentioning

confidence: 99%

A bat algorithm for SDN network scheduling

Lin

2018

J Wireless Com Network

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“…Cuckoo Search is meta-heuristic method based [15] on the obligate brood parasitic behavior of some cuckoo species. BAT is a novel algorithm inspired by [16] the behavior of bats. Artificial Fish Swarm Algorithm (AFSA) is developed by observing long on the fish nature using the swarm intelligence basing [17] on the imitating behavior of the fish such as preying, swarming etc.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Das¹,

Mishra²,

Rout³

2017

JESTR

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Financial forecasting is one of the imperative fields of research, where investors invest money and get restless for the future changes of the stock values in the market. In the recent course of time forecasting stock price is one of the challenging tasks. To predict the stock price most Artificial Neural Network (ANN) based model are used in the historical data along with statistical measures, technical indicators etc. With the development of ANNs, investors are hoping the best prediction because networks have great capability of machine learning problems such as classification and prediction. ANNs particularly Back Propagation (BP) has overlooked the non-stationary and noise characteristics of stock market data, as the training of BP is intricate due to the noise data and the network fall into a natural solution such as always predict an usual output. Most optimization techniques have been used for training the weights of forecasting models. Since no single optimization technique is invariably superior to others. Recently various nature inspired optimization techniques have been introduced and successfully employed to many fields of Financial Engineering. This survey aims to study the mostly used Bio-inspired optimization techniques on Stock Market Prediction and makes a comparative analysis between them.

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“…They have also been successfully used to solve optimization problems in the engineering, the financial, and the management fields. In 2010, Tsai et al propose Evolved Bat Algorithm (EBA) [11] based on utilizing the basic structure of Bat Algorithm (Yang, 2010) [12], [13] and re-estimating the characters used in BA. The details of EBA are given as follows.…”

Section: Evolved Bat Algorithm (Eba)mentioning

confidence: 99%

“…Furthermore, we use ISCI to select 15 investment targets from the candidates for later usage in the second phase. In the second phase, EBA (Tsai et al, 2011) [11] is exploited to optimize the capital allocation for investing on the selected targets. The purpose of this work is to provide the investors an investment portfolio with optimum reward.…”

Section: Introductionmentioning

confidence: 99%

Stock Portfolio Construction Using Evolved Bat Algorithm

Chang

Yang

Tsai

2014

Modern Advances in Applied Intelligence

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Abstract. Investment portfolio construction is always a popular issue for the investors. In this paper, we utilize Investment Satisfied Capability Index (ISCI) to sieve out the potential candidate stocks and then use Evolved Bat Algorithm (EBA) to construct the portfolio for stock investment. Three years historical daily Return on Investment (ROI) data from 2008 to 2010 is included in the experiment in order to test and verify the performance of the constructed stock portfolio by EBA. The experimental result is compared with the investment results from Taiwan broader market and Taiwan Top 50 Tracker Fund of Taiwan Yuanta Securities. The experimental result indicates that our proposed approach provides an efficient portfolio for the stock investment.

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Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

Cited by 113 publications

References 7 publications

A bat algorithm for SDN network scheduling

A bat algorithm for SDN network scheduling

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

Stock Portfolio Construction Using Evolved Bat Algorithm

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