Review: Multi-objective optimization methods and application in energy saving

【臨床医学の展望 2012】 呼吸器病学

Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

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A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

Feature selection plays a vital role in building machine learning models. Irrelevant features in data affect the accuracy of the model and increase the training time needed to build the model. Feature selection is an important process to build Intrusion Detection System (IDS). In this paper, a wrapper feature selection algorithm for IDS is proposed. This algorithm uses the pigeon inspired optimizer to utilize the selection process. A new method to binarize a continuous pigeon inspired optimizer is proposed and compared to the traditional way for binarizing continuous swarm intelligent algorithms. The proposed algorithm was evaluated using three popular datasets: KDDCUP99, NLS-KDD and UNSW-NB15. The proposed algorithm outperformed several feature selection algorithms from state-of-the-art related works in terms of TPR, FPR, accuracy, and F-score. Also, the proposed cosine similarity method for binarizing the algorithm has a faster convergence than the sigmoid method.

A feature selection algorithm for intrusion detection system based on Pigeon Inspired Optimizer

Optimization can be defined as an effort of generating solutions to a problem under bounded circumstances. Optimization methods have arisen from a desire to utilize existing resources in the best possible way. An important class of optimization methods is heuristic algorithms. Heuristic algorithms have generally been proposed by inspiration from the nature. For instance, Particle Swarm Optimization has been inspired by social behavior patterns of fish schooling or bird flocking. Bat algorithm is a heuristic algorithm proposed by Yang in 2010 and has been inspired by a property, named as echolocation, which guides the bats' movements during their flight and hunting even in complete darkness. In this work, local and global search characteristics of bat algorithm have been enhanced through three different methods. To validate the performance of the Enhanced Bat Algorithm (EBA), standard test functions and constrained real-world problems have been employed. The results obtained by these test sets have proven EBA superior to the standard one. Furthermore, the method proposed in this study is compared with recently published studies in the literature on real-world problems and it is proven that this method is more effective than the studies belonging to other literature on this sort of problems.

A new modification approach on bat algorithm for solving optimization problems

Purpose – The purpose of this paper is to present a novel swarm intelligence optimizer — pigeon-inspired optimization (PIO) — and describe how this algorithm was applied to solve air robot path planning problems. Design/methodology/approach – The formulation of threat resources and objective function in air robot path planning is given. The mathematical model and detailed implementation process of PIO is presented. Comparative experiments with standard differential evolution (DE) algorithm are also conducted. Findings – The feasibility, effectiveness and robustness of the proposed PIO algorithm are shown by a series of comparative experiments with standard DE algorithm. The computational results also show that the proposed PIO algorithm can effectively improve the convergence speed, and the superiority of global search is also verified in various cases. Originality/value – In this paper, the authors first presented a PIO algorithm. In this newly presented algorithm, map and compass operator model is prese...

http://hbduan.buaa.edu.cn/papers/IJICC_DuanHB_PIO_2014.pdf

Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning

Among the high-radix and low-diameter networks, fat-tree topology is commonly used in HPC and datacenter systems. Resource and job management is critically important to mitigate application interference in order to achieve high system performance and utilization. Preliminary studies have shown the effect of job placement on parallel scientific applications performance. In this work we study interference about job placement and routing algorithms on fat-tree system. Applications can be classified into various groups according to the communication patterns. We further combine various job placement policies and routing algorithms and create six different configurations. The system performance is analyzed by performing fine-grained high-fidelity discrete event-driven simulation. Initial experimentation shows that the performance of HPC applications not only is related with its communication pattern, but also relies on the job placement and network routing on fat-tree systems.

Preliminary Interference Study About Job Placement and Routing Algorithms in the Fat-Tree Topology for HPC Applications

Among the high-radix and low-diameter networks, fat-tree topology is commonly used in high-performance computing (HPC) and datacenter systems. Resource and job management on HPC systems is critically important to mitigate application interference in order to achieve high system performance and utilization. Preliminary studies have shown the effect of job placement on parallel scientific applications performance in fat-tree network. In this work we explore the joint effects of job placement and network routing aware of applications communication pattern on fat-tree system. Applications can be classified into various groups according to the communication patterns. We further combine various job placement policies and routing algorithms and create six different configurations. The system performance is analyzed using communication, hops, traffic, and saturation data by performing fine-grained high-fidelity discrete event-driven simulation. Initial experimentation shows that the performance of HPC applications not only is related with the communication pattern, but also relies on the job placement and network routing on fat-tree systems.

https://dl.acm.org/doi/pdf/10.1145/3229710.3229747

Peixin Qiao

Papers

Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning

Preliminary Interference Study About Job Placement and Routing Algorithms in the Fat-Tree Topology for HPC Applications

Joint Effects of Application Communication Pattern, Job Placement and Network Routing on Fat-Tree Systems