Fuzzy Multi-objective Particle Swarm Optimization Research Articles

Optimization of time based fuzzy multi-objective reliability redundancy allocation problem for [formula omitted] system using tuning and neighborhood based fuzzy MOPSO algorithm

In this paper, a time based fuzzy multi objective reliability redundancy allocation problem (FMORRAP) is proposed for the xj−out−of−m series–parallel system. The main objective is to maximize the system reliability with minimization of system cost and system repairing cost by optimizing the number of redundant components at each stage. The objectives are achieved by satisfying entropy constraints with limited redundant components at each stage and same for the whole system. Here the uncertainty of reliability, cost and repairing cost of each component is maintained by using triangular fuzzy number (TFN). The decreasing factor of component reliability and cost follows the change in the length of radius for the inverse logarithmic spiral with respect to time. Similarly the increasing factor of component repairing cost follows the same for the logarithmic spiral. Tuning and Neighborhood based Fuzzy Multi-Objective Particle Swarm Optimization (TNF-MOPSO) algorithm is proposed to solve fuzzy multi-objective optimization problems (MOOP). The proposed problem and proposed algorithm are illustrated by using a bench mark problem. The proposed algorithm produced better membership of objective functions for most of the time values and high satisfaction level of reliability for all values of the time parameter. It also outperforms the standard approaches MOPSO and NF-MOPSO.

An improved MOPSO algorithm for multi-objective optimization of reservoir operation under climate change.

Gradually, the previously proposed water resource management schemes and reservoir operating policies adjusted to the historically experienced climatic conditions are losing their validity and efficacy, urging building up the models compatible with the likely climatic change conditions at the future. This paper aims at optimizing the reservoir operation under climate change conditions targeting the objectives including (1) minimizing the shortages in meeting the reservoir downstream water demands and (2) maximizing the sustainability of the reservoir storage. For evaluating the effects of the climate change, six general circulation models (GCMs) built up under the representative concentration pathway (RCP4.5) emission scenario are adopted and utilized to predict the climate variables over a 30-year planning period. To solve this problem, an improved version of our recently proposed fuzzy multi-objective particle swarm optimization (f-MOPSO) algorithm, named f-MOPSO-II, is proposed. The f-MOPSO takes a novel approach to handle multi-objective nature of the optimization problems. In this approach, the common concept of "diversity" is replaced with "extremity," to choose the better guides of the search agents in the algorithm. The f-MOPSO-II is based on the f-MOPSO. However, it is aimed at simultaneously mitigating the f-MOPSO computational complexity and enhancing the quality of the final results presented by this algorithm. The results obtained by the f-MOPSO-II were then compared with those yielded by the popular non-dominated sorting genetic algorithm-II (NSGA-II). As the results suggest, the f-MOPSO-II is capable of simultaneously meeting the water demands and holding the reservoir storage sustainable, much better than the NSGA-II.

F-MOPSO/Div: an improved extreme-point-based multi-objective PSO algorithm applied to a socio-economic-environmental conjunctive water use problem.

In this paper, a new version of the multi-objective particle swarm optimizer named the Diversity-enhanced fuzzy multi-objective particle swarm optimization (f-MOPSO/Div) algorithm is proposed. This algorithm is an improved version of our recently proposed f-MOPSO. In the proposed algorithm, a new characteristic of the particles in the objective space, which we named the "extremity," is also evaluated, along with the Pareto dominance, to appoint proper guides for the particles in the search space. Three improvements are applied to the f-MOPSO to mitigate its shortcomings, generating f-MOPSO/Div: (1) selecting the global best solution based on the diversity of the extreme solutions, (2) impeding the particles to be trapped in the local optima using a mutation scheme based on the dynamic probability, and (3) removing the pre-optimization process. To validate f-MOPSO/Div, it was compared with some other popular multi-objective algorithms on 14 standard low- and high-dimensional test problem suites. After the comparative results indicated the outperformance of the proposal, the f-MOPSO/Div was applied to solve an optimal conjunctive water use management problem, in a semi-arid study area in west-central Iran, over a 13-year long-term planning period with two main objectives: (1) maximizing the aquifer sustainability as an environmental goal, and (2) maximizing the crop yields as a socio-economic goal. As the results suggest, the cumulative groundwater level drawdown is considerably decreased over the whole planning period to make the aquifer sustainable, while the water productivity is held at a desirable level, demonstrating the superiority of the f-MOPSO/Div when also applied to solve a large-scale real-world optimization problem.

Fuzzy Multi-objective Particle Swarm Optimization Solving the Three-Objective Portfolio Optimization Problem

In this paper, the portfolio optimization problem is approached. It is a NP-hard problem that consists in periodically creating an instance of the problem, using the time series of the shares value of a stock exchange. The instance is solved to determine the shares set that maximize the return, minimize the risk and minimize the number of selected shares. As far as we know, only three algorithms of the state-of-the-art of the portfolio selection problem with three objectives have been assessed. In this work, we propose a Fuzzy Multi-objective Particle Swarm Optimization (FOMOPSO) that uses an auto-Tuning Fuzzy Controller. To validate our approach, a series of experiments with the realistic instances and the performance of the proposed algorithm were compared with three state-of-the-art evolutionary algorithms using six commonly used metrics. To support the conclusions, two hypothesis tests were applied. The results show that the Fuzzy Rules auto-configuration contributes to that the proposed algorithm performance clearly outperforms three of the algorithms in comparison, for four of the metrics used.

Economic-Environmental Optimal Management of Smart Residential Micro-Grid Considering CCHP System

Due to issues such as air pollution and global warming, Distributed Energy Resources (DERs) including Distributed Generations (DGs), Energy Storage Systems (ESSs) and Demand Response Programs (DRPs) are gaining increasing attention to reduce dependency on fossil fuels as well as environmental pollution. In this article, a multi-objective optimization of economic environmental scheduling of DERs in a smart residential Micro-Grid (MG) considering numerous constraints and Demand Response Programs (DRPs) has been presented. To optimize the aforementioned objective functions, a new algorithm called Adaptive Fuzzy Multi-Objective Particle Swarm Optimization (AFMOPSO) has been proposed. This algorithm has a great capability to avoid local optimal solutions by finding a compromise between the general and local search within the decision making area. In addition, a comprehensive Combined Cooling Heating and Power (CCHP) system based on various thermodynamic equations has been proposed. The results demonstrate the efficient performance of the proposed algorithm compared to the other traditional optimization methods via some diversity and spacing criteria.

F-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management

In recent years, evolutionary techniques have been widely used to search for the global optimum of combinatorial non-linear non-convex problems. In this paper, we present a new algorithm, named fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO) to improve conjunctive surface water and groundwater management. The f-MOPSO algorithm is simple in concept, easy to implement, and computationally efficient. It is based on the role of weighting method to define partial performance of each point (solution) in the objective space. The proposed algorithm employs a fuzzy inference system to consider all the partial performances for each point when optimizing the objective function values. The f-MOPSO algorithm was compared with two other well-known MOPSOs through a case study of conjunctive use of surface and groundwater in Najafabad Plain in Iran considering two management models, including a typical 12-month operation period and a 10-year planning horizon. Overall, the f-MOPSO outperformed the other MOPSO algorithms with reference to performance criteria and Pareto-front analysis while nearly fully satisfying water demands with least monthly and cumulative groundwater level (GWL) variation. The proposed algorithm is capable of finding the unique optimal solution on the Pareto-front to facilitate decisions to address large-scale optimization problems.

An Integrated Approach for Intrusion Detection using Computational Methods

Today’s network world is facing lot of challenging task; Intrusion Detection is the method adopted to identify the unauthorised activities in the network. The type of malicious activities grows on increasing. There is very much essential to develop a new Intrusion Detection System, which can detect the malicious activities. The main purpose of our newly hybrid system is developed to identify both known and unknown attack. The proposed system is tested with the benchmark KDD ’99 intrusion data set. The proposed work also focuses on the detection rate and false alarm rate. The new system is developed with an optimized algorithm for feature to produce the reduced set of features. The attack detection rate is comparatively good; can be achieved by using layered approach with enhanced fuzzy multi-objective particle swarm optimization which does the feature selection effectively. The fuzzy based support vector machine algorithm is effectively applicable to detect anomaly attack. The newly described system is must more efficient in detection U2R attack, when compared with the existing methods takes long time for training to detect the unknown attack. Also our system is working with very less time for detection. The proposed system with the advantage of detection rate up to 99.1% and the false alarm rate is very much reduced.Keywords: Attack, Computation, Fuzzy, Goodfit, Intrusion Detection, Objective Function, Optimization, Support Vector Machine

A fuzzy multi-objective particle swarm optimization for effective data clustering

Data clustering, also called unsupervised learning, is a fundamental issue in data mining that is used to understand and mine the structure of an untagged assemblage of data into separate groups based on their similarity. Recent studies have shown that clustering techniques that optimize a single objective may not provide satisfactory result because no single validity measure works well on different kinds of data sets. Moreover, the performance of clustering algorithms degrades with more and more overlaps among clusters in a data set. These facts have motivated us to develop a fuzzy multi-objective particle swarm optimization framework in an innovative fashion for data clustering, termed as FMOPSO, which is able to deliver more effective results than state-of-the-art clustering algorithms. The key challenge in designing FMOPSO framework for data clustering is how to resolve cluster assignments confusion with such points in the data set which have significant belongingness to more than one cluster. The proposed framework addresses this problem by identification of points having significant membership to multiple classes, excluding them, and re-classifying them into single class assignments. To ascertain the superiority of the proposed algorithm, statistical tests have been performed on a variety of numerical and categorical real life data sets. Our empirical study shows that the performance of the proposed framework (in both terms of efficiency and effectiveness) significantly outperforms the state-of-the-art data clustering algorithms.

Multi-objective fuzzy particle swarm optimization based on elite archiving and its convergence *

A fuzzy particle swarm optimization (PSO) on the basis of elite archiving is proposed for solving multi-objective optimization problems. First, a new perturbation operator is designed, and the concepts of fuzzy global best and fuzzy personal best are given on basis of the new operator. After that, particle updating equations are revised on the basis of the two new concepts to discourage the premature convergence and enlarge the potential search space; second, the elite archiving technique is used during the process of evolution, namely, the elite particles are introduced into the swarm, whereas the inferior particles are deleted. Therefore, the quality of the swarm is ensured. Finally, the convergence of this swarm is proved. The experimental results show that the nondominated solutions found by the proposed algorithm are uniformly distributed and widely spread along the Pareto front.