Comparison of simulation-based methods and metaheuristic optimization algorithms for optimizing window design by considering daylighting and heat transfer in a tropical region of Indonesia

This study presents evaluation and comparison of simulation-based methods and metaheuristic optimization algorithms on building design models, focussing on daylight availability maximization and energy consumption minimization. The simulation-based method was presented using Rhino/Grasshopper software supported by the Ladybug, Honeybee, and Octopus optimization plugins; while MOPSO was chosen to calculate the metaheuristic optimization algorithm. The result indicated that OTTV values of the optimum design were respectively in the range of 24.06 W/m2 to 34.15 W/m2 for Octopus optimization and 25.19 W/m2 to 34.99 W/m2 for MPSO; and the WWR value for Octopus optimization and MOPSO were in the range 15% to 23% and 15% to 26%, respectively. While both methods showed similar results, the time duration for simulating in Rhino/Grasshopper was much longer compared to calculating the algorithm using MATLAB, indicating that simulation-based was less effective.

Social Network Search for Solving Engineering Optimization Problems

In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user’s efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including imitation, conversation, disputation, and innovation, are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views. The SNS algorithm was verified with 14 benchmark engineering optimization problems and one real application in the field of remote sensing. The performance of the proposed method is compared with various algorithms to show its effectiveness over other well-known optimizers in terms of computational cost and accuracy. In most cases, the optimal solutions achieved by the SNS are better than the best solution obtained by the existing methods.

An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm

<abstract><p>With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.</p></abstract>