• Nuclear Engineering and Technology
• /
• 제55권5호
• /
• pp.1838-1854
• /
• 2023

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A^* (IACO-A^*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A^* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A^* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A^* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A^* IACO-A^* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권8호
• /
• pp.3512-3528
• /
• 2016

A network with high mobility nodes or vehicles is vehicular ad hoc Network (VANET). For improvement in communication efficiency of VANET, many techniques have been proposed; one of these techniques is vehicular node clustering. Cluster nodes (CNs) and Cluster Heads (CHs) are elected or selected in the process of clustering. The longer the lifetime of clusters and the lesser the number of CHs attributes to efficient networking in VANETs. In this paper, a novel Clustering algorithm is proposed based on Ant Colony Optimization (ACO) for VANET named ACONET. This algorithm forms optimized clusters to offer robust communication for VANETs. For optimized clustering, parameters of transmission range, direction, speed of the nodes and load balance factor (LBF) are considered. The ACONET is compared empirically with state of the art methods, including Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering techniques. An extensive set of experiments is performed by varying the grid size of the network, the transmission range of nodes, and total number of nodes in network to evaluate the effectiveness of the algorithms in comparison. The results indicate that the ACONET has significantly outperformed the competitors.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
• /
• 제2권5호
• /
• pp.191-198
• /
• 2013

이동하는 베이스 노드를 가진 무선 센서 네트워크(WSN)에서 실시간 침입탐지를 위해서는 침입을 탐지한 센서로부터 베이스 노드까지의 정보 전달이 짧은 라우팅 경로를 통해 이루어져야 한다. 센서 네트워크에서 최소 Wiener수 신장트리(MWST)기반 라우팅 방법은 최소 신장트리(MST)기반 라우팅 방법에 비해 작은 홉 수를 보장하고 있어서 실시간 침입탐지에 적합함이 알려져 있다. 하지만 주어진 네트워크로부터 최소 Wiener 수 신장트리를 찾는 문제는 NP-hard이고 특정 노드에 대한 의존성이 커서 최소 신장 트리 기반 라우팅 방법에 비해 짧은 네트워크 수명을 갖는 단점이 있다. 본 논문은 실시간 침입탐지를 위해 최소 Wiener수 신장트리를 개선해 작은 홉 수와 긴 네트워크의 수명을 동시에 보장하는 라우팅 트리를 찾는 다목적 개미 군집 최적화 알고리즘을 제안한다. 그리고 제안한 라우팅 트리의 성능을 패킷의 평균 전송 홉 수 및 네트워크 전력 소모, 네트워크의 수명 측면에서 최소 신장트리기반 라우팅 방법 및 최소 Wiener수 신장트리기반 라우팅 방법과 비교한다.