• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.944-951
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• 2015

Pulse Width Modulation (PWM) techniques are increasingly employed for PWM inverter fed induction motor drive. Among various popular PWM methods used, Selective Harmonic Elimination PWM (SHEPWM) has been widely accepted for its better harmonic elimination capability. In addition, using SHEPWM, it is also possible to maintain better voltage regulation. Hence, in this paper, an attempt has been made to apply Bacterial Foraging Algorithm (BFA) for solving selective harmonic elimination problem. The problem of voltage harmonic elimination together with output voltage regulation is drafted as an optimization task and the solution is sought through proposed method. For performance comparison of BFA, the results obtained are compared with other techniques such as derivative based Newton-Raphson method, and Genetic Algorithm. From the comparison, it can be observed that BFA based approach yields better results. Further, it provides superior convergence, reduced computational burden, and guaranteed global optima. The simulation results are validated through experimental findings.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.569-570
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• 2007

본 논문에서는 대장균 먹이찾기(E. Coli Bacterial Foraging)를 최적화 기법에 도입한 사례로서 이를 이용하여 Nonconvex 배전용 경제급전 알고리즘(distribution economic dispatch: DED을 제안한다. 제안된 DED 알고리즘은 향후 지역이나 구역 발전사업자를 중심으로 배전계통운용시스템에 필요한 경제급전 알고리즘으로 활용 할 수 있다.

• ETRI Journal
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• v.40 no.6
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• pp.726-735
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• 2018

Femtocells are good examples of the ultimate networking technology, offering enhanced indoor coverage and higher data rate. However, the dense deployment of femto base stations (FBSs) and the exploitation of subcarrier reuse between macrocell base stations and FBSs result in significant co-tier and cross-tier interference, thus degrading system performance. Therefore, appropriate resource allocations are required to mitigate the interference. This paper proposes a discrete bacterial foraging optimization (DBFO) algorithm to find the optimal resource allocation in two-tier networks. The simulation results showed that DBFO outperforms the random-resource allocation and discrete particle swarm optimization (DPSO) considering the small number of steps taken by particles and bacteria.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.241-244
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• 2007

본 논문에서는 박테리아의 주화성에 기초한 Bacteria Cooperative Optimization(BCO) 알고리즘을 소개한다. BCO는 Ant Colony Optimization (ACO)처럼 자연계에 존재하는 생명체의 행동양식을 모방하여 만든 최적화 알고리즘으로 크게 초기화, 측정, 행동결정, 이동으로 구성된다. 우리는 먼저 BCO 알고리즘을 설명하고 2차원 함수 최적화 문제를 이용하여 BCO알고리즘과 Genetic Algorithm(GA) 그리고 Bacterial Foraging for Distributed Optimization(BFO)의 성능 측정 결과를 기술한다. 실험 결과 BCO의 성능이 GA나 BFO보다 우수함을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1437-1459
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• 2020

Wireless Sensor Networks (WSN) is a distributed Sensor network whose terminals are sensors that can sense and check the environment. Sensors are typically battery-powered and deployed in where the batteries are difficult to replace. Therefore, maximize the consumption of node energy and extend the network's life cycle are the problems that must to face. Low-energy adaptive clustering hierarchy (LEACH) protocol is an adaptive clustering topology algorithm, which can make the nodes in the network consume energy in a relatively balanced way and prolong the network lifetime. In this paper, the novel multi-objective LEACH protocol is proposed, in order to solve the proposed protocol, we design a multi-objective coupling algorithm based on bat algorithm (BA), glowworm swarm optimization algorithm (GSO) and bacterial foraging optimization algorithm (BFO). The advantages of BA, GSO and BFO are inherited in the multi-objective coupling algorithm (MBGF), which is tested on ZDT and SCH benchmarks, the results are shown the MBGF is superior. Then the multi-objective coupling algorithm is applied in the multi-objective LEACH protocol, experimental results show that the multi-objective LEACH protocol can greatly reduce the energy consumption of the node and prolong the network life cycle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.442-460
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• 2021

Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE⁺ are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1535-1547
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• 2016

This paper proposes various design procedures for computing Power System Ancillary Service Requirement Assessment Indices (PSASRAI) for a Two-Area Thermal Reheat Interconnected Power System (TATRIPS) in a restructured environment. In an interconnected power system, a sudden load perturbation in any area causes the deviation of frequencies of all the areas and also in the tie-line powers. This has to be corrected to ensure the generation and distribution of electric power companies to ensure good quality. A simple Proportional and Integral (PI) controllers have wide usages in controlling the Load Frequency Control (LFC) problems. So the design of the PI controller gains for the restructured power system are obtained using Bacterial Foraging Optimization (BFO) algorithm. From the simulation results, the PSASRAI are calculated based on the settling time and peak over shoot concept of control input deviations of each area for different possible transactions. These Indices are useful for system operator to prepare the power system restoration plans. Moreover, the LFC loop coordinated with Redox Flow Batteries (RFB) has greatly improved the dynamic response and it reduces the control input requirements and to ensure improved PSASRAI, thereby improving the system reliability.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.182-195
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• 2023

Recently, research on traffic signal control using artificial intelligence algorithms has been receiving attention, and many traffic signal control models are being studied. However, most studies either focused on independent intersections or are theoretical studies that calculate signal cycle length according to changes in traffic volume. Therefore, this study was conducted on a signalized intersection - roundabout in Gajwa-ro. The Particle Swarm Optimization - Bacterial Foraging Optimization (PSO-BFO) algorithm was proposed, which is developed from the GA and PSO algorithms for minimizing congestion at two intersections. As a result, optimum cycle length was determined to be 158 seconds. The Verkehr In Stadten - SIMulationsmodell (VISSIM) results showed that there was 3.4% increased capacity, 8.2% reduced delay and 8.3% reduced number of stops at the Gajwa-ro signalized intersection. Additionally, at the roundabout, a 9.2% increase in capacity, a 7.1% reduction in delay, and a 27.2% decrease in the number of stops was observed.