Adaptive neighbourhood for locally and globally tuned biogeography based optimization algorithm

Giri, Parimal Kumar; De, Sagar S.; Dehuri, Satchidananda

doi:10.1016/j.jksuci.2018.03.013

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…It's difficult to strike a balance between exploration and exploitation because of the low population diversity and convergence rate. An extensive study of BBO algorithms can be found in [28].…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

“…The parameter α is known as a geographical system's immaturity index [28]. This is inversely related to the system's invasion resistance.…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

“…As compared to various evolutionary algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and differential evolution (DE), BBO requires information sharing between solutions [28].…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

A Locally and Globally Tuned Metaheuristic Optimization for Overlapping Community Detection

Mallick¹,

Giri²,

Mishra³

2023

MJCS

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Many people use online social networks to share their opinions and information in this digital age. The number of people engaged and their dynamic nature pose a major challenge for social network analysis (SNA). Community detection is one of the most critical and fascinating issues in social network analysis. Researchers frequently employ node features and topological structures to recognize important and meaningful performance in order to locate non-overlapping communities. We introduce a locally and globally tuned multi-objective biogeography-based optimization (LGMBBO) technique in this research for detecting overlapping communities based on the number of connections and node similarity. Four real- world online social network datasets were used in the experiment to assess the quality of both overlapping and non-overlapping partitions. As a result, the model generates a set of solutions that have the best topological structure of a network with node properties. The suggested model will increase their productivity and enhance their abilities to identify significant and pertinent communities.

show abstract

Section: Biogeography-based Optimizationmentioning

confidence: 99%

“…The parameter α is known as a geographical system's immaturity index [28]. This is inversely related to the system's invasion resistance.…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

A Locally and Globally Tuned Metaheuristic Optimization for Overlapping Community Detection

Mallick¹,

Giri²,

Mishra³

2023

MJCS

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“…Instead, the original, two unique mechanisms have been to change the Suitability Index Variables (SIVs). To deal effectively with current challenge, Parimal Kumar Giri et al [44] developed a novel migration model for BBO, named as ANLGBBO. To obtain a strong universal applicability of BBO, Xinming et al [45] presented a novel hybrid algorithm named HBBOG, by using BBO and GWO algorithms.…”

Section: Where: T=current Iteration T=maximum Iterationmentioning

confidence: 99%

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

Sharma

Saxena²,

Palwalia

2022

Technol Econ Smart Grids Sustain Energy

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Demand Response (DR) keeps up an important character in the growth of electricity generation with transmission and distribution in the phase of advanced grid automation. For future smart grid, basically two types of Demand Side Management strategies have been used with a number of shiftable or controllable devices. In this research article, two new variants of Biogeography Based Optimization (BBO) algorithm named as Iteration Control BBO and Pandemic BBO are proposed for solving the minimization problem and definitions of these variants have been made by change in migration control � ⃗ a vector. These two variants are indirectly support the principle of No Free Lunch theorem. The beauty of these variants is that they are reported the impact of divergence in BBO on efficient energy strategy of DSM. The simulation results reported in this paper claims that the used DSM techniques acquire financial savings, while lowering and shifting peak load.

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“…Furthermore, it has also no sufficient attributes for balancing between exploration and exploitation of the search space. To cope with these challenges, we have developed a variant of novel BBO algorithms, namely, locally and globally tuned BBO (LGBBO) (Giri et al, 2017a), Chaotic LGBBO (LGCBBO) (Giri et al, 2017b), and adaptive neighborhood for LGBBO (ANL-GBBO) (Giri et al, 2018). Therefore, by keeping in mind to improve the performance along with exploiting the accumulated search space and explore the large region to identify high-quality solutions, the locally and globally tuned BBO (LGBBO) algorithm (authors one of the improved BBO algorithms) has been inducted in this work for uncovering if-then classification rules from a credit scoring database.…”

Section: Related Workmentioning

confidence: 99%

Biogeography based optimization for mining rules to assess credit risk

Giri

Dehuri

et al. 2021

Intell Sys Acc Fin Mgmt

Self Cite

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Financial institutions, by and large, rely on the use of machine learning techniques to improve the classic credit risk assessment model for reduction of costs, delivery of faster decisions, guaranteed credit collections, and risk mitigations. As such, several data mining and machine learning approaches have been developed for computation of credit scores over the last few decades. Moreover, the existing rule-based classification algorithms tend to generate a number of rules with a large number of conditions in the antecedent part. However, these algorithms fail to demonstrate high predictive accuracy while balancing coverage and simplicity. Thus, it becomes quite a challenging task for the researchers to generate an optimal rule set with high predictive accuracy. In this paper, we present an effective rule based classification technique for the prediction of credit risk using a novel Biogeography Based Optimization (BBO) method. The novel BBO in the context of rule mining is named as locally and globally tuned biogeography based rule-miner (LGBBO-RuleMiner). This is applied for discovering optimal rule set with high predictive accuracy from the dataset containing both the categorical and continuous attributes. The performance of the proposed algorithm is compared against a variety of rule-miners such as OneR (1R), PART, JRip, Decision Table, Conjunctive Rule, J48, and Random Tree, along with some meta-heuristic based rule mining techniques by considering two credit risk datasets obtained from University of California, Irvine (UCI) repository. It is found from the comparative study that the proposed rule miner in ten independent runs of tenfold cross validation outperforms all of the aforesaid algorithms in terms of predictive accuracy, coverage, and simplicity.

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Adaptive neighbourhood for locally and globally tuned biogeography based optimization algorithm

Cited by 7 publications

References 32 publications

A Locally and Globally Tuned Metaheuristic Optimization for Overlapping Community Detection

A Locally and Globally Tuned Metaheuristic Optimization for Overlapping Community Detection

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

Biogeography based optimization for mining rules to assess credit risk

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