OPTIMIZATION OF FIRE MONITORING SYSTEM USING THE DEFORMED STARS METHOD

In this paper are being considered the aspects of two variables function optimization problem solving, which, in general, is poly-extremal and undifferentiated. The classic methods of continuous optimization are not applicable in this case. One of the most commonly used methods of solving this problem is evolutionary algorithms, which can be divided into two classes. The first class includes algorithms where a potential offspring-solution is generated by two parent-solutions solutions, in the second case, the offspring-solution is generated by one parent-solution. There is deformed star method proposed where the population of parental solutions is 3, 4, and 5 point groups. The application of proposed method is shown to solve the optimization problem of fire monitoring system for buildings, which minimizes the time of its operation. The buildings where fire load can be both permanent and variable are considered. Such buildings include concert halls, nightclubs, supermarkets, logistics facilities and more. Fires at such buildings result in human sacrifice and serious material loss. Timely activation of the fire alarm system have great importance. The objective function of the problem is determined by the distance from the horizontal projections of the detectors to the sources of fire and the probability of triggering the detectors. The solution is optimizing location of fire detectors, taking into account their number and the fire load of the room. The advantages of the developed method over genetic algorithms, evolutionary strategies and differential evolution as the most typical evolutionary algorithms are shown. Numerical experiments were carried out, which showed the increased accuracy of calculations and the increased speed of method convergence.

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Development of Multiple Behaviors in Evolving Robots

Robotics ◽

10.3390/robotics10010001 ◽

2020 ◽

Vol 10 (1) ◽

pp. 1

Author(s):

Victor Massagué Respall ◽

Stefano Nolfi

Keyword(s):

Evolutionary Algorithms ◽

State Of The Art ◽

Evolutionary Robotics ◽

The Other ◽

Evolutionary Strategies ◽

Multiple Behaviors ◽

Pareto Fronts ◽

Expected Fitness

We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered.

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An enhanced chimp optimization algorithm for continuous optimization domains

Complex & Intelligent Systems ◽

10.1007/s40747-021-00346-5 ◽

2021 ◽

Author(s):

Heming Jia ◽

Kangjian Sun ◽

Wanying Zhang ◽

Xin Leng

Keyword(s):

Social Status ◽

Optimization Algorithm ◽

Continuous Optimization ◽

Rank Correlation ◽

Function Optimization ◽

Local Optimum ◽

Hunting Behavior ◽

Spearman’S Rank Correlation ◽

Engineering Design Problems ◽

Application Fields

AbstractChimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.

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An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽

10.1155/2018/3051854 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 7

Author(s):

Jing Xiao ◽

Jing-Jing Li ◽

Xi-Xi Hong ◽

Min-Mei Huang ◽

Xiao-Min Hu ◽

...

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Portfolio Optimization ◽

Optimization Problem ◽

Experimental Comparison ◽

Software Project ◽

Project Portfolio ◽

Multiobjective Evolutionary Algorithms ◽

Reference Distance ◽

Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

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A Study of Remote Monitoring Fire Alarm System Based on Internet of Things

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.977.330 ◽

2014 ◽

Vol 977 ◽

pp. 330-333

Author(s):

Qi Lin Hao ◽

Ping Xian Yang ◽

Ming Jie Wang ◽

Rui Ma

Keyword(s):

Remote Monitoring ◽

Data Transmission ◽

Short Distance ◽

Alarm System ◽

Transmission Network ◽

Wireless Data ◽

Fire Alarm ◽

Fire Monitoring ◽

Wireless Data Transmission ◽

Fire Alarm System

In this paper, in order to achieve real-time remote monitoring of fire site, we designed a intelligent remote fire monitoring system based on ZigBee network and GPRS network. This system, used ZigBee and sensor to build data acquisition and short distance transmission network and combined with GPRS wireless data transmission and SMS alarm ,cooperates with the server and PC to build a remote monitoring scheme based on GRPS.

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A Fire Monitoring System In ZigBee Wireless Network

IEEE Transactions on Microwave Theory and Techniques ◽

10.1109/tmtt.2010.2077891 ◽

2010 ◽

Vol 58 (11) ◽

pp. 2751-2763 ◽

Cited By ~ 30

Author(s):

Kuduck Kwon ◽

Jaeyoung Choi ◽

Jeongki Choi ◽

Yongseok Hwang ◽

Kwyro Lee ◽

...

Keyword(s):

Wireless Network ◽

Monitoring System ◽

Fire Monitoring

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FireBird: A Fire Alert and Live Fire Monitoring System Based on Social Media Contribution

Lecture Notes in Computer Science - Cooperative Design, Visualization, and Engineering ◽

10.1007/978-3-030-60816-3_12 ◽

2020 ◽

pp. 104-114

Author(s):

Arijit Das ◽

Rahul Dutta ◽

Ayan Dey ◽

Thomas Tamisier ◽

Sukriti Bhattacharya

Keyword(s):

Social Media ◽

Monitoring System ◽

Fire Monitoring

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Heliostat Field Layout Optimization with Evolutionary Algorithms

10.29007/7p6t ◽

2018 ◽

Author(s):

Pascal Richter ◽

David Laukamp ◽

Levin Gerdes ◽

Martin Frank ◽

Erika Ábrahám

Keyword(s):

Power Plant ◽

Convex Optimization ◽

Evolutionary Algorithms ◽

Convergence Rate ◽

Evolutionary Algorithm ◽

Computer Science ◽

Energy Supply ◽

Optimization Problem ◽

Convex Optimization Problem ◽

New Genotype

The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for optimization. This work addresses the problem of finding an optimal heliostat field arrangement for a solar tower power plant.We propose a solution to this global, non-convex optimization problem by using an evolutionary algorithm. We show that the convergence rate of a conventional evolutionary algorithm is too slow, such that modifications of the recombination and mutation need to be tailored to the problem. This is achieved with a new genotype representation of the individuals.Experimental results show the applicability of our approach.

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