Firefly Swarm: Metaheuristic Swarm Intelligence Technique for Mathematical Optimization

Durbhaka, Gopi Krishna; Barani, S.; Nayyar, Anand

doi:10.1007/978-981-13-1274-8_34

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…Эти методы содержат элемент случайности и позволяют решать широкий класс современных задач оптимизации строительных конструкций. Известен ряд подходов к оптимизации, таких как методы роя частиц [17][18][19], отжига [20,21], светлячков [22,23], муравьиной колонии [24,25], теплопередачи [26], взрыва [27], поиска гармонии [28], генетические алгоритмы [29][30][31], нейронные сети [32][33][34] и т.п. Ряд из них имеет существенные ограничения для применения к задачам оптимизации строительных конструкций, поэтому в опубликованных научных работах рассматриваются лишь элементарные примеры.…”

Section: основная часть обзораunclassified

Review of modern optimization methods for bearing systems of buildings аnd structures

Tamrazyan

Alekseytsev

2020

Vestnik MGSU

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Introduction. A review of modern approaches to the search for design solutions, optimal by various criteria, for load-bearing construction structures is given. The paper discloses problems of finding solutions on discrete sets of variable parameters reflecting modern approaches to design considering the available range of materials and products. It also formulated the goals of the literary review, its relevance to modern socio-economic conditions, and a brief historical digest reflecting the stages of development of search methods. The focus of the review is on new metaheuristic approaches that are used in world practice to optimize the load-bearing systems. Materials and methods. The methods of searching for relevant literature in international scientific databases are described. The selection of sources was based on the international Web of Science databases by publishing house Thomson Reuters, Scopus, EI Compendex by Elsevier publishing house which, had well-developed tools for thematic search as well as guaranteeing the legitimacy and quality of the literature. Results. The article classified the modern methods of searching for optimal design solutions by several criteria. Those included the metaheuristic approach type, number of optimization goals, methods of considering constraints, presentation of varying parameters, consideration of life cycle stages, survivability, reliability, facility safety levels, and accident risks. A series of the most promising optimization methods based on the mathematical formalization of physical processes, as well as combined iterative strategies, are described. Attention is also paid to some gradient approaches and methods of mathematical programming. Conclusions. The paper has shown that to meet modern requirements for building load-bearing structures, including safety criteria, minimizing costs during the life cycle, reliability, survivability, and other properties, the methods similar to biological processes in wildlife are most widely spread. These methods have a relatively fast adaptability to solve a broad class of problems in construction. Analysis of the reviewed sources showed that the far-reaching methods for solving problems of construction design optimization are stochastic methods using discrete sets of design parameters that reflect features of building structure design.

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Section: основная часть обзораunclassified

Review of modern optimization methods for bearing systems of buildings аnd structures

Tamrazyan

Alekseytsev

2020

Vestnik MGSU

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“…Nayyar et al [51][52][53][54][55][56][57] have provided a detailed understanding on evolutionary algorithms including GA, and swarm based algorithms including ACO, ABO, PSO, Glow worm, Cockroach swarm optimization, Cat swarm optimization, Dolphin echo location, Eagle strategy, monkey search algorithm etc., highlighting the various computational models, the versatile approaches along with their applications in the newly emerging complex fields of engineering including IOT, AI, Big data, Data mining and Robotics. Panda et al [23] have provided a detailed overview of the popular metaheuristic algorithms since last two decades including Cuckoo Search(CS), Gravitational search algorithm(GSA), Genetic Algorithms(GA), Particle swarm optimization(PSO), Differential Evolution(DE) and Artificial Bee Colony algorithm(ABC) and have compared the performances of the algorithms to generate test data for path coverage based testing.…”

Section: Literature Reviewmentioning

confidence: 99%

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

et al. 2020

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In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FADE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm's performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.

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“…These methods contain an element of randomness and allow solving a wide class of modern problems for the optimization of load-bearing structures. A number of optimization approaches are known, such as particle swarm methods [1][2][3], annealing [4,5], fireflies [6,7], ant colony [8,9], heat transfer [10], explosion [11], harmony search [12], genetic algorithms [13][14][15], neural networks [16][17][18], etc. A number of them have significant limitations for application to the problems of optimization of load-bearing structures, taking into account the level of their safety; in many published scientific works, only elementary examples are considered.…”

Section: Introductionmentioning

confidence: 99%

Risk-Based Optimization of RC-Beams Under Different Local Damage Scenarios

Alekseytsev,

Kurchenko,

Mohamad

2023

Advances in Transdisciplinary Engineering

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A methodology for optimizing rod systems is proposed, taking into account the criterion of mechanical safety in the event of emergency actions. Based on evolutionary modeling, an iterative procedure for finding the optimal parameters of structures that have a minimum cost at the maximum level of mechanical safety is con-structed. As such a level of safety, it is proposed to use the value of the ratio of the cost of material damage to the cost of the structure and the condition of resistance to progressive destruction. The search for optimal solutions obtained using the proposed methodology can significantly reduce the consequences of accidents in construction, which can be caused by both natural and man-made fac-tors. The use of this optimization approach has prospects for use both for newly designed load-bearing bar structures and for operating systems with local damage.

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Firefly Swarm: Metaheuristic Swarm Intelligence Technique for Mathematical Optimization

Cited by 12 publications

References 21 publications

Review of modern optimization methods for bearing systems of buildings аnd structures

Review of modern optimization methods for bearing systems of buildings аnd structures

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

Risk-Based Optimization of RC-Beams Under Different Local Damage Scenarios

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