Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems

USING FEATURE SELECTION AND ACO ALGORITHM FOR OPTIMIZING SMART CLASSROOM

The smart education had a huge impact on learning and teaching, so it must be effective and highly efficient. An efficient smart campus or smart classroom will make the learning more and more easily, the students could learn and give the best activities. In addition, the teachers will be able to make right decisions. To achieve this goal, the smart classroom's conditions must be ideal. Since ACO (ant colony optimization algorithm) is a meta heuristic algorithm, in this paper, it is found that ACO, in conjunction with a machine learning classifier, was an effective method used in feature selection for selecting best features from an intelligent campus data set to create an environment that is conducive to academic success and student learning, such as (humidity and temperature), lighting and sound pressure levels, wind direction, and raw rainfall amounts (among other variables). In this contribution to get the most accurate results, the ACO algorithm was combined with a logistic regression classifier that was used to select the best features. The accuracy of the proposed model was 0.927438624 and 0.898268071 for two sets of data back to the School of Design and Environment 4, building located at the National University of Singapore

JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem

In recent years, optimization problems have been intriguing in the field of computation and engineering due to various conflicting objectives. The complexity of the optimization problem also dramatically increases with respect to a complex search space. Nature-Inspired Optimization Algorithms (NIOAs) are becoming dominant algorithms because of their flexibility and simplicity in solving the different kinds of optimization problems. Hence, the NIOAs may be struck with local optima due to an imbalance in selection strategy, and which is difficult when stabilizing exploration and exploitation in the search space. To tackle this problem, we propose a novel Java macaque algorithm that mimics the natural behavior of the Java macaque monkeys. The Java macaque algorithm uses a promising social hierarchy-based selection process and also achieves well-balanced exploration and exploitation by using multiple search agents with a multi-group population, male replacement, and learning processes. Then, the proposed algorithm extensively experimented with the benchmark function, including unimodal, multimodal, and fixed-dimension multimodal functions for the continuous optimization problem, and the Travelling Salesman Problem (TSP) was utilized for the discrete optimization problem. The experimental outcome depicts the efficiency of the proposed Java macaque algorithm over the existing dominant optimization algorithms.