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首页> 外文期刊>Arabian Journal for Science and Engineering >Improved Firefly Algorithm Based on Genetic Algorithm Operators for Energy Efficiency in Smart Buildings
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Improved Firefly Algorithm Based on Genetic Algorithm Operators for Energy Efficiency in Smart Buildings

机译：基于遗传算法算子的改进型Firefly算法在智能建筑中的能效

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Firefly algorithm (FA) is an easily implementable, robust, simple and flexible technique, but the major drawback associated with this technique is the imbalanced exploration and exploitation during firefly position changing stage. This imbalanced relation degrades the solution quality which ultimately results in either skipping the most optimal solution even present in the vicinities of the current solution or trapping the solution in the local optima. In this paper, this issue is resolved by introducing genetic algorithm (GA) operators namely selection, mutation and crossover operators in the firefly position stage of the standard FA. The performance of the proposed approach has been tested on energy consumption optimization and user comfort management inside smart building and has been compared with the standard FA, GA, artificial bee colony (ABC) and ant colony optimization (ACO) algorithm in terms of temperature, illumination, air quality and total power consumption minimization and user comfort maximization. The minimum, average, maximum and total power consumption and minimum, maximum and average user comfort were the performance evaluation parameters. The least amount of 145.39 kilowatt hour (kWh) of total power consumed for temperature control was observed for the proposed approach followed by ACO, ABC, FA and GA where the power consumed for temperature was observed as 173.68kWh, 179.27kWh, 181.93kWh and 188.95kWh, respectively. Similarly, for illumination control, the consumed power for the proposed model was 118.30kWh followed by FA, ABC, ACO and GA where the power consumed was 146.93kWh, 162.96kWh, 169.28kWh and 193.53kWh, respectively. For air quality control, the minimum power of 186.94kWh was found for the proposed algorithm followed by FA, ABC, ACO and GA where the power consumed was 229.01kWh, 234.38kWh, 240.47kWh and 244.76kWh, respectively. Likewise, maximum user comfort was observed for the proposed technique with the value of 0.94004/1 followed by ACO, ABC, FA and GA where the user comfort recorded was 0.939655/1, 0.93878/1, 0.938314/1 and 0.937896/1, respectively. The statistical analysis shows the efficiency of the proposed model for power consumption minimization and user comfort maximization.
机译：萤火虫算法（FA）是一种易于实现，健壮，简单且灵活的技术，但与该技术相关的主要缺点是萤火虫位置更改阶段的勘探与开发不平衡。这种不平衡的关系降低了解决方案的质量，最终导致要么跳过甚至存在于当前解决方案附近的最佳解决方案，要么将解决方案陷入局部最优状态。本文通过在标准FA的萤火虫定位阶段引入遗传算法（GA）运算符（即选择，变异和交叉运算符）解决了该问题。该提议方法的性能已在智能建筑内的能耗优化和用户舒适度管理方面进行了测试，并已在温度，温度，温度，性能，性能，性能，性能以及与标准FA，GA，人工蜂群（ABC）和蚁群优化（ACO）算法进行了比较，照明，空气质量和总功耗最小化，用户舒适度最大化。最小，平均，最大和总功耗以及最小，最大和平均用户舒适度是性能评估参数。对于该方法，观察到的用于控制温度的总功率最小为145.39千瓦时（kWh），其次是ACO，ABC，FA和GA，其中观察到的功率为173.68kWh，179.27kWh，181.93kWh和分别为188.95kWh。类似地，对于照明控制，所提议模型的功耗为118.30kWh，其次是FA，ABC，ACO和GA，其功耗分别为146.93kWh，162.96kWh，169.28kWh和193.53kWh。对于空气质量控制，建议算法的最小功率为186.94kWh，其次是FA，ABC，ACO和GA，其功耗分别为229.01kWh，234.38kWh，240.47kWh和244.76kWh。同样，对于建议的技术，观察到的最大用户舒适度值为0.94004 / 1，其次是ACO，ABC，FA和GA，其中记录的用户舒适度分别为0.939655 / 1、0.93878 / 1、0.938314 / 1和0.937896 / 1 。统计分析显示了所提出模型的功耗最小化和用户舒适度最大化的效率。

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