• 제목/요약/키워드: Ant colony optimization algorithm

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CO2 emissions optimization of reinforced concrete ribbed slab by hybrid metaheuristic optimization algorithm (IDEACO)

  • Shima Bijari;Mojtaba Sheikhi Azqandi
    • Advances in Computational Design
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    • 제8권4호
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    • pp.295-307
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    • 2023
  • This paper presents an optimization of the reinforced concrete ribbed slab in terms of minimum CO2 emissions and an economic justification of the final optimal design. The design variables are six geometry variables including the slab thickness, the ribs spacing, the rib width at the lower and toper end, the depth of the rib and the bar diameter of the reinforcement, and the seventh variable defines the concrete strength. The objective function is considered to be the minimum amount of carbon dioxide gas (CO2) emission and at the same time, the optimal design is economical. Seven significant design constraints of American Concrete Institute's Standard were considered. A robust metaheuristic optimization method called improved dolphin echolocation and ant colony optimization (IDEACO) has been used to obtain the best possible answer. At optimal design, the three most important sources of CO2 emissions include concrete, steel reinforcement, and formwork that the contribution of them are 63.72, 32.17, and 4.11 percent respectively. Formwork, concrete, steel reinforcement, and CO2 are the four most important sources of cost with contributions of 67.56, 19.49, 12.44, and 0.51 percent respectively. Results obtained by IDEACO show that cost and CO2 emissions are closely related, so the presented method is a practical solution that was able to reduce the cost and CO2 emissions simultaneously.

돌연변이 개미 군집화 알고리즘을 이용한 스마트 물류 창고의 다중 주문 처리 시스템 (Muti-Order Processing System for Smart Warehouse Using Mutant Ant Colony Optimization)

  • 김창현;김근태;김여진;이종환
    • 반도체디스플레이기술학회지
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    • 제22권3호
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    • pp.36-40
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    • 2023
  • Recently, in the problem of multi-order processing in logistics warehouses, multi-pickup systems are changing from the form in which workers walk around the warehouse to the form in which goods come to workers. These changes are shortening the time to process multiple orders and increasing production. This study considered the sequence problem of which warehouse the items to be loaded on each truck come first and which items to be loaded first when loading multiple pallet-unit goods on multiple trucks in an industrial smart logistics automation warehouse. To solve this problem efficiently, we use the mutant algorithm, which combines the GA algorithm and ACO algorithm, and compare with original system.

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하이브리드 병렬 유전자 알고리즘을 이용한 최적 신뢰도-중복 할당 문제 (An Optimal Reliability-Redundancy Allocation Problem by using Hybrid Parallel Genetic Algorithm)

  • 김기태;전건욱
    • 산업공학
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    • 제23권2호
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    • pp.147-155
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    • 2010
  • Reliability allocation is defined as a problem of determination of the reliability for subsystems and components to achieve target system reliability. The determination of both optimal component reliability and the number of component redundancy allowing mixed components to maximize the system reliability under resource constraints is called reliability-redundancy allocation problem(RAP). The main objective of this study is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for reliability-redundancy allocation problem that decides both optimal component reliability and the number of component redundancy to maximize the system reliability under cost and weight constraints. The global optimal solutions of each example are obtained by using CPLEX 11.1. The component structure, reliability, cost, and weight were computed by using HPGA and compared the results of existing metaheuristic such as Genetic Algoritm(GA), Tabu Search(TS), Ant Colony Optimization(ACO), Immune Algorithm(IA) and also evaluated performance of HPGA. The result of suggested algorithm gives the same or better solutions when compared with existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improve solution through swap, 2-opt, and interchange processes. In order to calculate the improvement of reliability for existing studies and suggested algorithm, a maximum possible improvement(MPI) was applied in this study.

컨테이너 터미널의 불확실한 환경 하에서의 ALV 주행 계획 수립방안 (Routing of ALVs under Uncertainty in Automated Container Terminals)

  • 김정민;이동균;류광렬
    • 한국항해항만학회지
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    • 제38권5호
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    • pp.493-501
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    • 2014
  • 무인 자가 운반 하역차량(Automated Lifting Vehicle, ALV)은 자동화 컨테이너 터미널에서 컨테이너를 수송하는 무인 차량의 하나로 자가 하역 및 수송 능력을 가지고 있다. 여러 대의 ALV를 이용해 컨테이너를 효율적으로 수송하기 위해서는 ALV가 컨테이너의 이송작업을 시작할 때마다 최소 시간에 주행이 가능한 경로를 실시간으로 찾을 수 있어야 한다. 또한 차량 간의 충돌 및 교착 상태 발생 시 스스로 해결이 불가능한 무인 차량의 특성 상 이러한 충돌 및 교착을 막을 수 있도록 차량이 목적지까지 가기 위해 점유해야 하는 점유 영역과 그 점유 시간을 결정하여 이를 겹치지 않도록 주행 계획을 수립하여야 한다. 하지만 주행 계획 수립을 위한 ALV의 점유 영역에서의 점유 시간 계산은 교통 상황에 따른 주행 시간의 변화나 주행 경로 상에 작업을 수행하는 크레인의 작업 상황의 불확실성 때문에 정확한 추정이 어렵다. 본 논문에서는 개미 집단 최적화 기법을 기반으로 이러한 ALV 도착 시간의 불확실성을 고려한 ALV 주행 계획 수립방안을 제안한다. 시뮬레이션 실험을 통해 제안 방안이 불확실한 환경에서 효율적으로 좋은 경로를 찾아냄을 확인하였다.

유전알고리즘을 이용한 지속가능 공간최적화 모델 기초연구 - 선행연구 분석을 중심으로 - (Basic Study on Spatial Optimization Model for Sustainability using Genetic Algorithm - Based on Literature Review -)

  • 윤은주;이동근
    • 한국환경복원기술학회지
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    • 제20권6호
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    • pp.133-149
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    • 2017
  • As cities face increasing problems such as aging, environmental pollution and growth limits, we have been trying to incorporate sustainability into urban planning and related policies. However, it is very difficult to generate a 'sustainable spatial plans' because there are trade-offs among environmental, society, and economic values. This is a kind of non-linear problem, and has limitations to be solved by existing qualitative expert knowledge. Many researches from abroad have used the meta heuristic optimization algorithms such as Genetic Algorithms(GAs), Simulated Annealing(SA), Ant Colony Optimization(ACO) and so on to synthesize competing values in spaces. GAs is the most frequently applied theory and have been known to produce 'good-enough plans' in a reasonable time. Therefore we collected the research on 'spatial optimization model based GAs' and analyzed in terms of 'study area', 'optimization objective', 'fitness function', and 'effectiveness/efficiency'. We expect the results of this study can suggest that 'what problems the spatial optimization model can be applied to' and 'linkage possibility with existing planning methodology'.

ANN을 이용한 절삭성능의 예측과 ACO를 이용한 훈련 (Prediction of Machining Performance using ANN and Training using ACO)

  • 오수철
    • 한국기계가공학회지
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    • 제16권6호
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    • pp.125-132
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    • 2017
  • Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

Multi-Collector Control for Workload Balancing in Wireless Sensor and Actuator Networks

  • Han, Yamin;Byun, Heejung
    • 대한임베디드공학회논문지
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    • 제16권3호
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    • pp.113-117
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    • 2021
  • The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

Tuning of a PID Controller Using Soft Computing Methodologies Applied to Basis Weight Control in Paper Machine

  • Nagaraj, Balakrishnan;Vijayakumar, Ponnusamy
    • 펄프종이기술
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    • 제43권3호
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    • pp.1-10
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    • 2011
  • Proportional.Integral.Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programming, and Particle Swarm Optimization and Ant colony optimization. The proposed algorithm is used to tune the PID parameters and its performance has been compared with the conventional methods like Ziegler Nichols and Lambda method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. This research addresses comparison of tuning of the PID controller using soft computing techniques on Machine Direction of basics weight control in pulp and paper industry. Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the soft computing based tuning method. The ability of the designed controller, in terms of tracking set point, is also compared and simulation results are shown.

Harmony Search 알고리즘의 수렴성 개선에 관한 연구 (Study on Improvement of Convergence in Harmony Search Algorithms)

  • 이상경;고광은;심귀보
    • 한국지능시스템학회논문지
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    • 제21권3호
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    • pp.401-406
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    • 2011
  • 복잡해진 최적화문제를 전통적인 방법보다 효율적으로 해결하기위해 유전알고리즘이나 개미군집화, 하모니서치알고리즘과 같은 다양한 메타휴리스틱이 개발되었다. 그 중에서 하모니 서치알고리즘이 다른 메타휴리스틱알고리즘보다 좋은 결과를 보이고 있다. 하모니 서치 알고리즘은 음악을 작곡할 때 아름다운 소리를 내는 하모니를 찾는 과정을 모방했다. 성능은 하모니 메모리에서 선택하는 비율인 HMCR값과 하모니 메모리에서 선택된 값의 조정 비율을 결정하는 PAR값에 따라 달라지는 것으로 알려져 있다. 다르게 말하면 두 변수의 기반이 되는 하모니 메모리의 사용방법의 문제로 볼 수 있다. 본 논문은 설정한 기간 동안 더 좋은 최적해를 찾지 못할 경우 하모니 메모리의 일부를 좋은 하모니로 구성되게 수정하는 방법을 제안했다. 테스트 함수를 이용한 검증 실험결과에서 하모니 메모리를 수정할 경우 정확도 변화가 적어 신뢰성 있는 정확도를 보였으며, Iteration이 짧더라도 최적값에 근접한 값을 찾았다.

Routing Protocol for Wireless Sensor Networks Based on Virtual Force Disturbing Mobile Sink Node

  • Yao, Yindi;Xie, Dangyuan;Wang, Chen;Li, Ying;Li, Yangli
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제16권4호
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    • pp.1187-1208
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    • 2022
  • One of the main goals of wireless sensor networks (WSNs) is to utilize the energy of sensor nodes effectively and maximize the network lifetime. Thus, this paper proposed a routing protocol for WSNs based on virtual force disturbing mobile Sink node (VFMSR). According to the number of sensor nodes in the cluster, the average energy and the centroid factor of the cluster, a new cluster head (CH) election fitness function was designed. At the same time, a hexagonal fixed-point moving trajectory model with the best radius was constructed, and the virtual force was introduced to interfere with it, so as to avoid the frequent propagation of sink node position information, and reduce the energy consumption of CH. Combined with the improved ant colony algorithm (ACA), the shortest transmission path to Sink node was constructed to reduce the energy consumption of long-distance data transmission of CHs. The simulation results showed that, compared with LEACH, EIP-LEACH, ANT-LEACH and MECA protocols, VFMSR protocol was superior to the existing routing protocols in terms of network energy consumption and network lifetime, and compared with LEACH protocol, the network lifetime was increased by more than three times.