A Review of Parameters for Improving the Performance of Particle Swarm Optimization

보안공학연구지원센터(IJHIT) 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.4

Particle swarm optimization (PSO) is an artificial intelligence (AI) technique that can be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems. Particle swarm optimization is an optimization method. It is an optimization algorithm, which is based on swarm intelligence. Optimization problems are widely used in different fields of science and technology. Sometimes such problems can be complex due to its practical nature. Particle swarm optimization (PSO) is a stochastic algorithm used for optimization. It is a very good technique for the optimization problems. But still there is a drawback that it gets stuck in local minima. To improve the performance of PSO, the researchers have proposed some variants of PSO. Some researchers try to improve it by improving the initialization of swarm. Some of them introduced new parameters like constriction coefficient and inertia weight. Some define different methods of the inertia weight to improve performance of PSO and some of them work on the global and local best. This paper transplants some of the parameters used to enhance the performance of Particle Swarm Optimization technique.

Multi-Objective Particle Swarm Optimization: An Introduction

Vipin Kumar,Sonajharia Minz 한국산학기술학회 2014 SmartCR Vol.4 No.5

In the real world, reconciling a choice between multiple conflicting objectives is a common problem. Solutions to a multi-objective problem are those that have the best possible negotiation given the objectives. An evolutionary algorithm called Particle swarm optimization is used to find a solution from the solution space. It is a population-based optimization technique that is effective, efficient, and easy to implement. Changes in the particle swarm optimization technique are required in order to get solutions to a multi-objective optimization problem. Therefore, this paper provides the proper concept of particle swarm optimization and the multi-objective optimization problem in order to build a basic background with which to conduct multi-objective particle swarm optimization. Then, we discuss multi-objective particle swarm optimization concepts. Multi-objective particle swarm optimization techniques and some of the most important future research directions are also included.

Detecting Sinkhole Attack in Wireless Sensor Network using Enhanced Particle Swarm Optimization Technique

G. Keerthana,G. Padmavathi 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.3

Wireless Sensor Network (WSN) is a collection of tiny sensor nodes capable of sensing and processing the data. These sensors are used to collect the information from the environment and pass it on to the base station. A WSN is more vulnerable to various attacks. Among the different types of attacks, sinkhole attack is more vulnerable because it leads to a variety of attacks further in the network. Intrusion detection techniques are applied to handle sinkhole attacks. One of effective approach of intrusion detection mechanism is using Swarm Intelligence techniques (SI). Particle Swarm Optimization is one of the important swarm intelligence techniques. This research work enhances the existing Particle Swarm Optimization technique and the proposed technique is tested in a simulated environment for performance. It is observed that the proposed Enhanced Particle Swarm Optimization (EPSO) technique performs better in terms of Detection rate, False Alarm rate, Packet delivery ration, Message drop and Average delay when compared to the existing swarm intelligence techniques namely, Ant Colony Optimization and Particle Swarm Optimization.

A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems

Hasan Koyuncu,Rahime Ceylan 한국CDE학회 2019 Journal of computational design and engineering Vol.6 No.2

In the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm Optimization (PSO) and an efficient oper-ator of Artificial Bee Colony Optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (Pbest). Thus, the need for external intervention is inevitable once a useful par-ticle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm Optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants; the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of BitABC is 2. In comparison with hybrid techniques, ScPSO obtains the best Total Average Rank (TAR) as 1.375, and TSS of ScPSO ranks first (6) again. The fitness values obtained by ScPSO are generally more satisfactory than the values obtained by other methods. Consequently, ScPSO achieve promising gains over other optimization methods; in parallel with this result, its usage can be extended to different working disciplines.

PSO의 다양한 영역 탐색과 지역적 미니멈 인식을 위한 전략

이영아,양성봉,김택헌 한국정보처리학회 2009 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.16 No.4

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle’s movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle’s movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions. PSO(Particle Swarm Optimization)는 군집(swarm)을 구성하는 단순한 개체들인 입자(particle)들이 각자의 경험을 공유하여 문제의 해답을 찾는 최적화 알고리즘으로 다양한 분야에서 응용되고 있다. PSO에 대한 연구는 최적화를 위해 군집이 적합한 영역으로 빠르게 수렴하도록 하는 파라미터 값의 선정, 토폴로지, 입자의 이동에서 주로 이루어지고 있다. 표준 PSO 알고리즘은 입자 자신과 최고의 이웃이 제공하는 정보만을 이용해서 이동하므로 다양한 영역을 탐색하지 못하고 지역적 최적점에 조기 수렴하는 경향이 있다. 본 논문에서는 군집이 다양한 영역을 탐색하기 위해, 각 입자는 더 나은 경험을 가진 이웃입자들의 정보를 상대적인 중요도에 따라서 참조하여 이동하도록 하였다. 다양한 영역의 탐색은 표준 PSO 알고리즘보다 지역적 최적화의 확률을 줄이고 탐색 속도를 가속화하며 탐색의 성공률을 높일 수 있다. 또한 군집이 지역적 미니멈으로부터 벗어나기 위한 검사 전략을 제안하여 탐색의 성공률을 높였다. 제안한 PSO 알고리즘을 평가하기 위하여, 벤치마크 함수들에 적용한 결과 최적화의 진행 속도 개선과 탐색 성공률의 향상이 있었다.

Research on Dynamic Cost-Sensitive SVM Classifier based on Chaos Particle Swarm Optimization Algorithm

Ruili Zhang 보안공학연구지원센터 2014 International Journal of Control and Automation Vol.7 No.10

In order to improve the performance of Support Vector Machine (SVM) classifier for imbalanced data, this paper proposes dynamic cost-sensitive SVM classifier based on chaos particle swarm optimization (CPDC_SVM). Firstly, this paper introduces dynamic cost-sensitive thought to SVM classifier, and gives the method for structuring dynamic cost and cost-sensitive SVM model. Secondly, we propose the evaluation methodology performance for classifier, and adopts decimal base to code the particles. At last, chaos thought is introduced in particle swarm optimization algorithm, and the Algorithm of the dynamic cost-sensitive SVM classifier is given, which improves convergent speed and accuracy of particle swarm optimization, and can optimize dynamic cost-sensitive SVM well, so CPDC_SVM adds effectively the convergence speed and accuracy for the particle swarm optimization algorithm. Experimental results show CPDC_SVM has higher precision than traditional SVM classifier, and dynamic cost and chaos particle swarm optimization can improve the performance for classifier.

PSO 알고리즘을 이용한 퍼지 Extreme Learning Machine 최적화

노석범(Seok-Beom Roh),王繼紅(Jihong Wang),김용수(Yong-Soo Kim),안태천(Tae-Chon Ahn) 한국지능시스템학회 2016 한국지능시스템학회논문지 Vol.26 No.1

본 논문에서는 일반적인 신경회로망의 단점인 느린 학습속도를 획기적으로 개선한 네트워크인 Extreme Learning Machine과 전문가들의 언어적 정보들을 기술 할 수 있는 퍼지 이론을 접목한 퍼지 Extreme Learning Machine을 최적화하기 위하여 Particle Swarm Optimization 알고리즘을 이용하였다. 퍼지 Extreme Learning Machine의 활성화 함수를 일반적인 시그모이드 함수를 사용하지 않고, 퍼지 C-Means 클러스터링 알고리즘의 활성화 레벨 함수를 이용하였다. Particle Swarm Optimization 알고리즘과 같은 최적화 알고리즘을 통하여 퍼지 Extreme Learning Machine의 활성화 함수의 파라미터들을 최적화 한다. Particle Swarm Optimization과 같은 최적화 알고리즘을 통한 제안된 모델의 최적화 하고 최적화된 모델의 분류성능을 평가하기 위하여 다양한 머신 러닝 데이터 집합을 사용하여 평가한다. In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

최적화 알고리즘을 이용한 정보데이터 분할방법에 대한 연구

장영훈(Young-Hun Jang),김진율(Jin-Yul Kim),오성권(Sung-Kwun Oh) 한국지능시스템학회 2018 한국지능시스템학회논문지 Vol.28 No.4

본 연구에서는 최적화 기법에 도움으로 설계된 개선된 교차 검증법을 소개한다. 교차 검증법은 적은 데이터를 가지고도 통계적 신뢰성을 높이기 위한 방법이고 개선된 교차 검증법은 최적화 기법에 알맞게 적용한 방법이다. 개선된 교차 검증법과 기존 교차 검증법을 이용하여 다양한 구조의 데이터 분할을 하고 이를 방사형 기저함수의 입력데이터로 사용한다. 은닉층을 FCM클러스터링 알고리즘기반의 RBFNNs을 분류기로 사용하고 은닉층의 연결가중치로는 규칙 후반부에 다항식 계수를 최소자승법으로 추정한다. RBFNNs에 사용되는 파라미터(예를 들면, 퍼지화 계수 클러스터의 개수) 뿐만 아니라 다항식 종류는 Multi Objective Particle Swarm Optimization와 Particle Swarm Optimization를 이용하여 최적화된다. 제안된 방법의 성능 평가를 위해 다양한 종류의 Machine Learning(ML)데이터를 사용하여 분류 성능을 구한다. 그리고 기존방법과 제안된 방법의 성능의 비교해석이 묘사된다. In this study, an improved cross validation method designed with the aid of optimization techni ues is introduced. The cross validation method is a method to improve the statistical reliability even with a small amount of data, and the improved cross validation method is applied to the optimization technique. By using both the improved cross validation method and the existing cross validation method, the data is divided into various structures and used as the input data of the radial basis function. RBFNNs based on FCM clustering algorithm are used as a classifier. as hidden weighting factors, the polynomial coefficients of the consequent part of rules are estimated using least square method. The parameters(viz. fuzzification coefficient and number of clusters) as well as polynomial type used in RBFNNs is optimized by using both Multi Objective Particle Swarm Optimization and Particle Swarm Optimization. To evaluate the performance of the proposed method, classification performance is obtained by using various kinds of Machine Learning (ML) dataset. The comparative analysis between the performance of the proposed method and that of the existing methods is described.

Empirical Analysis of Effect of Particle Swarm Optimization Inertia Weight Strategies over Particle Swarm Optimization with Aging Leader and Challengers

Anu Sharma,Mandeep Kaur 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.11

Particle swarm optimization is the optimization technique motivated by swarm intelligence and aims to find the best solution in the swarm. Aging leader and challengers with Particle swarm optimization (ALC-PSO) is a population based optimization method which introduced the concept of aging and challenger generation in the PSO technique. This variant of PSO has been successful in preventing premature convergence of PSO and maintaining swarm diversity. In this paper, we briefly reviewed the inertia weight parameter and its strategies in PSO and experimentally analyzed the effect of inertia weight strategies on ALC-PSO performance. Comparison is drawn between PSO and ALC-PSO based on these strategies. Results are obtained using five different benchmark functions.

An integrated particle swarm optimizer for optimization of truss structures with discrete variables

Ali Mortazavi,Vedat Toğan,Ayhan Nuhoğlu 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.61 No.3

This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.