Concurrent bacterial foraging with emotional intelligence for global optimization

The integration of concurrent bacterial foraging with emotional PSO known as concurrent bacterial foraging with emotional intelligence (CBFEI) is proposed in this paper. This technique is used to optimize the functions with multiple local optima with high dimensions and real time applications with l...

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Vydáno v:International journal of information technology (Singapore. Online) Ročník 11; číslo 2; s. 313 - 320
Hlavní autoři: Nagpal, Renu, Singh, Parminder, Garg, B. P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Singapore 04.06.2019
Springer Nature B.V
Témata:
Algorithms
Bacteria
Computer Communication Networks
Computer Science
Computer Systems Organization and Communication Networks
Convergence
Emotional intelligence
Global optimization
Information Systems and Communication Service
Management of Computing and Information Systems
Mutation
Original Research
Performance degradation
Psychology
Software Engineering/Programming and Operating Systems
Joyful
Mutation
Analysis of variance test (ANOVA)
Sad
Concurrent bacterial foraging with emotional intelligence (CBFEI)
ISSN:2511-2104, 2511-2112
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Shrnutí:The integration of concurrent bacterial foraging with emotional PSO known as concurrent bacterial foraging with emotional intelligence (CBFEI) is proposed in this paper. This technique is used to optimize the functions with multiple local optima with high dimensions and real time applications with less computational cost and better accuracy. In original BFO, the bacteria positions are updated sequentially and its performance is degraded due to fixed step size. But in CBFEI, positions of bacteria are updated concurrently, which is called as concurrent bacterial foraging and mutation is used for dynamic step size to attain accurate optima with fast convergence. The psychology factors of emotion such as joyful and sad are introduced in CBF, which is treated as mutation based on emotional intelligence. The joyful bacterium enjoys in reproducing more accurate global best while bacterium will shrink from its current position, if it is sad. The premature convergence is avoided by mutation. The seven benchmark functions are used to validate the performance of CBFEI. The different evaluation parameters and ANOVA are used to compare the results of CBFEI with other optimization algorithms. The proposed technique achieves more accurate results in terms of optimum solution and better convergence as compared to other techniques.
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ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-018-0215-z