Spiral Dynamics Inspired Optimization

Tamura, Kenichi; Yasuda, Keiichiro

doi:10.20965/jaciii.2011.p1116

Cited by 98 publications

(26 citation statements)

References 6 publications

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“…Spiral optimization method has been recently developed based on the analogy to spiral phenomena (Tamura and Yasuda 2011a; Tamura and Yasuda 2011b). …”

Section: The Spiral Inspired Optimization Methodsmentioning

confidence: 99%

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Optimal multiobjective design of digital filters using spiral optimization technique

2013

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The multiobjective design of digital filters using spiral optimization technique is considered in this paper. This new optimization tool is a metaheuristic technique inspired by the dynamics of spirals. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The objectives of filter design include matching some desired frequency response while having minimum linear phase; hence, reducing the time response. The results demonstrate that the proposed problem solving approach blended with the use of the spiral optimization technique produced filters which fulfill the desired characteristics and are of practical use.

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“…Spiral optimization method has been recently developed based on the analogy to spiral phenomena (Tamura and Yasuda 2011a; Tamura and Yasuda 2011b). …”

Section: The Spiral Inspired Optimization Methodsmentioning

confidence: 99%

“…Logarithmic spirals discrete processes to generate spirals that can form an effective behaviour in metaheuristics. A two-dimensional algorithm has been first proposed (Tamura and Yasuda 2011a), and then, a more generalized n-dimensional version has been recently suggested (Tamura and Yasuda 2011b). …”

Section: The Spiral Inspired Optimization Methodsmentioning

confidence: 99%

Optimal multiobjective design of digital filters using spiral optimization technique

2013

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“…Heuristic algorithms are commonly used to solve nonlinear and more complicated optimization problems that make it popular in the computational process [28]. Simple structure, ease of use, quick results, local searching capacity, and few control variables are some advantages of spiral dynamic optimization [27], [28].…”

Section: Spiral Dynamic Optimizationmentioning

confidence: 99%

Comparison of Dengue Transmission in Lowland and Highland Area: Case Study in Semarang and Malang, Indonesia

Fauzi

Fakhruddin

Nuraini

et al. 2019

Communication in Biomathematical Sciences

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Dengue is a potentially lethal mosquito-borne disease, regarded as the most dangerous disease in the world. It is also a major health issue in tropical and subtropical countries. Environmental characteristics and sociocultural are factors which play a role in the spread of dengue. Different landscape structure such as lowland and highland areas are possible to give different infection rate on dengue transmission. Semarang and Malang are densely populated areas in Java, which are selected to be our study areas. A mathematical model (SIR-UV) is adapted to describe dengue transmission. Spiral dynamic optimization is applied to convert monthly data to weekly in Malang and estimate the infection rate that minimized the deviation between dengue data and simulation. This method produces a good fitting to the data. We compare the pattern of dengue cases from the simulation in both cities. Furthermore, we identify seasonal variations of the cases via Fourier series of the infection rate. We also investigate the correlation between humidity, infection rate, and dengue cases in Semarang and Malang. It reveals that humidity influences infection rate in 1-3 weeks later and the infection rate produces dengue cases in the next four weeks.

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“…This section briefly reviews the SPO algorithm [6], [8], a nature-inspired metaheuristic based on spiral phenomena in nature.…”

Section: Outline Of Spo Algorithmmentioning

confidence: 99%

“…With this as background, the spiral optimization (SPO) algorithm, a metaheuristic inspired by spiral phenomena in nature, was proposed by K. Tamura and K. Yasuda to solve continuous optimization problems [5], [6]. Spiral phenomena are spirals approximating logarithmic spirals ( Fig.…”

Section: Introductionmentioning

confidence: 99%

Spiral Optimization Algorithm Using Periodic Descent Directions

Tamura

Yasuda

2016

SICE Journal of Control, Measurement, and System Integration

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: A few years ago, the authors proposed a nature-inspired metaheuristic concept, the spiral optimization algorithm, which was inspired by spiral phenomena in nature. The principal idea of the algorithm is to utilize spiral trajectories generated by multiple generalized spiral models for search applications. The generalized spiral model is composed of a spiral matrix defined by a composite rotation matrix and a convergence rate parameter. The setting of the spiral matrix with each initial point placement is important for its search performance, because it characterizes each spiral trajectory. This paper proposes 1) the concept of periodic descent directions for a spiral trajectory that is appropriate for optimization; 2) sufficient conditions, with examples, for the generalized spiral model to generate the periodic descent directions; 3) a setting method for the composite rotation matrix with initial search points to satisfy the conditions in the algorithm; and 4) a method for setting the convergence rate parameter to utilize the periodic descent directions effectively for its search performance. The effectiveness of the proposed method is confirmed by conducting numerical experiments under various conditions.

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Spiral Dynamics Inspired Optimization

Cited by 98 publications

References 6 publications

Optimal multiobjective design of digital filters using spiral optimization technique

Optimal multiobjective design of digital filters using spiral optimization technique

Comparison of Dengue Transmission in Lowland and Highland Area: Case Study in Semarang and Malang, Indonesia

Spiral Optimization Algorithm Using Periodic Descent Directions

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