2012/11/04 by Mohammed Ali Tawfeeq, Tawfeeq, Mohammed Ali · 2 citations
Computer Science · Earth and Planetary Sciences · Engineering · Environmental Science · Mathematics · #Algorithm #Artificial intelligence #Computer science #Convergence (economics) #Engineering #FOS: Computer and information sciences #Genetic algorithm #Local optimum #Mathematical optimization #Mathematics #Neural and Evolutionary Computing (cs.NE) #Point (geometry) #Precipitation Measurement and Analysis #Range (aeronautics) #Sonar #Space (punctuation) #State (computer science) #State space #Term (time) #Underwater Vehicles and Communication Systems #Unit (ring theory) #Water Quality Monitoring Technologies #Water Systems and Optimization #cs.NE
paper · pdf · doi:10.48550/arxiv.1211.0730
published in arXiv (Cornell University) (Cornell University) · 9 pages, 16 figures, 7 tables; (IJCSIS) International Journal of Computer Science and Information Security,Vol. 10, No. 10, October 2012
arxiv created 2012/11/04 · openalex publication_date 2012/11/04 · arxiv updated 2012/11/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper presents a new intelligent algorithm that can solve the problems\nof finding the optimum solution in the state space among which the desired\nsolution resides. The algorithm mimics the principles of bat sonar in finding\nits targets. The algorithm introduces three search approaches. The first search\napproach considers a single sonar unit (SSU) with a fixed beam length and a\nsingle starting point. In this approach, although the results converge toward\nthe optimum fitness, it is not guaranteed to find the global optimum solution\nespecially for complex problems; it is satisfied with finding 'acceptably good'\nsolutions to these problems. The second approach considers multisonar units\n(MSU) working in parallel in the same state space. Each unit has its own\nstarting point and tries to find the optimum solution. In this approach the\nprobability that the algorithm converges toward the optimum solution is\nsignificantly increased. It is found that this approach is suitable for complex\nfunctions and for problems of wide state space. In the third approach, a single\nsonar unit with a moment (SSM) is used in order to handle the problem of\nconvergence toward a local optimum rather than a global optimum. The momentum\nterm is added to the length of the transmitted beams. This will give the chance\nto find the best fitness in a wider range within the state space. In this paper\na comparison between the proposed algorithm and genetic algorithm (GA) has been\nmade. It showed that both of the algorithms can catch approximately the optimum\nsolutions for all of the testbed functions except for the function that has a\nlocal minimum, in which the proposed algorithm's result is much better than\nthat of the GA algorithm. On the other hand, the comparison showed that the\nrequired execution time to obtain the optimum solution using the proposed\nalgorithm is much less than that of the GA algorithm.\n