Interval multi-objective quantum-inspired cultural algorithms

Neural Computing and Applications - It had been proved that the knowledge may promote more efficient evolution. Considering the knowledge defined in different form, we present interval...     

Dynamic multi-objective evolutionary algorithms for single-objective optimization

This paper proposes a new method for handling the difficulty of multi-modality for the single-objective optimization problem (SOP). The method converts a SOP to an equivalent dynamic multi-objective optimization problem (DMOP). A new dynamic multi-objective evolutionary algorithm (DMOEA) is implemented to solve the DMOP. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the multi-modality difficulty during the search process. Experimental results show that the performance of the proposed algorithm is significantly better than the state-of-the-art competitors on a set of benchmark problems and real world antenna array problems.     

Emergent nature inspired algorithms for multi-objective optimization

Many real-world decision-making situations possess both a discrete and combinatorial structure and involve the simultaneous consideration of conflicting objectives. Problems of this kind are in general of large size and contains several objectives to be “optimized”. Although Multiple Objective Optimization is a well-established field of research, one branch, namely nature inspired metaheuristics is currently experienced a tremendous growth. Over the last few years, developments of new methodologies, methods, and techniques to deal with multi-objective large size problems in particular those with a combinatorial structure and the strong improvement on computing technologies (during and after the 80s) made possible to solve very hard problems with the help of inspired nature based metaheuristics.     

一类区间多目标粒子群优化算法

针对区间参数多目标优化问题,提出一种基于模糊支配的多目标粒子群优化算法。首先,定义基于决策者悲观程度的模糊支配关系,用于比较解的优劣;然后,定义一种适于区间目标值的拥挤距离,以更新外部存储器并从中选择领导粒子;最后,对多个区间多目标测试函数进行仿真实验,实验结果验证了所提出算法的有效性。     

利用MISA多目标优化的置信规则库分类算法

现有基于置信规则库的分类系统的分类准确率和效率受到系统参数设置以及规则库结构合理性的影响。为了寻找到最佳的参数值和最优的规则库结构,本文结合多目标免疫系统算法（multiobjective immune system algorithm, MISA）提出利用MISA多目标优化的置信规则库分类算法。该方法融合特征属性约简思想和差分进化算法思想建立训练模型,采用多目标免疫系统算法对系统复杂度和分类准确率进行多目标优化,从而寻找到分类模型的最优解。在实验分析中,首先将本文提出的置信规则库多目标分类系统MISA-BRM和置信规则库分类系统的实验结果进行对比,从复杂度和准确率两个维度说明本文方法的有效性。同时还将本文方法与现有的其他分类方法进行比较,验证本文方法的可行性和有效性。实验结果表明,本文方法能够有效地对基于置信规则库的分类系统的准确率和复杂度进行多目标优化。     

基于NSGA-II的扩展置信规则库激活规则多目标优化方法

针对扩展置信规则库（extended belief rule base,EBRB）系统在不一致的激活规则过多时推理准确性不高的问题,引入带精英策略的快速非支配排序遗传算法（NSGA-Ⅱ）,提出一种基于NSGA-Ⅱ的激活规则多目标优化方法。该方法首先将激活权重大于零的规则（即激活规则）进行二进制编码,把最终参与合成推理的激活规则集合的不一致性以及激活权重和作为多目标优化问题的目标函数,通过带精英策略的快速非支配排序遗传算法求解不一致性更小的激活规则集合,从而降低不一致激活规则对于EBRB系统推理准确性的影响。为了验证本文方法的有效性和可行性,引入非线性函数和输油管道检漏实例进行测试。实验结果表明,基于NSGA-Ⅱ的扩展置信规则库激活规则多目标优化方法能够有效提高EBRB系统的推理能力。     

Optimal residual design for fault diagnosis using multi-objective optimization and genetic algorithms

This paper develops a new approach to the design of optimal residuals in order to diagnose incipient faults based on multi-objective optimization and genetic algorithms. In this approach the residual is generated via an observer. To reduce false and missed alarm rates in fault diagnosis, a number of performance indices are introduced into the observer design. Some performance indices are expressed in the frequency domain to take account of the frequency distributions of faults, noise and modelling uncertainties. All objectives then are reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search for an optimal solution to satisfy these inequality constraints on performance indices. The approach developed is applied to a flight control system example, and simulation results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.     

A comparative study of the multi-objective optimization algorithms for coal-fired boilers

Combustion optimization has been proved to be an effective way to reduce the NOx emissions and unburned carbon in fly ash by carefully setting the operational parameters of boilers. However, there is a trade-off relationship between NOx emissions and the boiler economy, which could be expressed by Pareto solutions. The aim of this work is to achieve multi-objective optimization of the coal-fired boiler to obtain well distributed Pareto solutions. In this study, support vector regression (SVR) was employed to build NOx emissions and carbon burnout models. Thereafter, the improved Strength Pareto Evolutionary Algorithm (SPEA2), the new Multi-Objective Particle Swarm Optimizer (OMOPSO), the Archive-Based hYbrid Scatter Search method (AbYSS), and the cellular genetic algorithm for multi-objective optimization (MOCell) were used for this purpose. The results show that the hybrid algorithms by combining SVR can obtain well distributed Pareto solutions for multi-objective optimization of the boiler. Comparison of various algorithms shows MOCell overwhelms the others in terms of the quality of solutions and convergence rate.     

Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm

Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature. Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders. We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach, extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly based intrusions. Experiments show that the proposed method is efficient in many scenarios.

A comparative study of multi-objective optimization algorithms for sparse signal reconstruction

The development of the efficient sparse signal recovery algorithm is one of the important problems of the compressive sensing theory. There exist many types of sparse signal recovery methods in compressive sensing theory. These algorithms are classified into several categories like convex optimization, non-convex optimization, and greedy methods. Lately, intelligent optimization techniques like multi-objective approaches have been used in compressed sensing. Firstly, in this paper, the basic principles of the compressive sensing theory are summarized. And then, brief information about multi-objective algorithms, local search methods, and knee point selection methods are given. Afterward, multi-objective sparse recovery methods in the literature are reviewed and investigated in accordance with their multi-objective optimization algorithm, the local search method, and the knee point selection method. Also in this study, examples of multi-objective sparse reconstruction methods are designed according to the existing studies. Finally, the designed algorithms are tested and compared by using various types of sparse reconstruction test problems.

     

Selection of initial designs for multi-objective optimization using classification and regression tree

One of the major challenges for solving large-scale multi-objective optimization design problems is to find the Pareto set effectively. Data mining techniques such as classification, association, and clustering are common used in computer community to extract useful information from a large database. In this paper, a data mining technique, namely, Classification and Regression Tree method, is exploited to extract a set of reduced feasible design domains from the original design space. Within the reduced feasible domains, the first generation of designs can be selected for multi-objective optimization to identify the Pareto set. A mathematical example is used to illustrate the proposed method. Two industrial applications are used to demonstrate the proposed methodology that can achieve better performances in terms of both accuracy and efficiency.     

Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection

In this paper, we consider a recently proposed model for portfolio selection, called Mean-Downside Risk-Skewness (MDRS) model. This modelling approach takes into account both the multidimensional nature of the portfolio selection problem and the requirements imposed by the investor. Concretely, it optimizes the expected return, the downside-risk and the skewness of a given portfolio, taking into account budget, bound and cardinality constraints. The quantification of the uncertain future return on a given portfolio is approximated by means of LR-fuzzy numbers, while the moments of its return are evaluated using possibility theory. The main purpose of this paper is to solve the MDRS portfolio selection model as a whole constrained three-objective optimization problem, what has not been done before, in order to analyse the efficient portfolios which optimize the three criteria simultaneously. For this aim, we propose new mutation, crossover and reparation operators for evolutionary multi-objective optimization, which have been specially designed for generating feasible solutions of the cardinality constrained MDRS problem. We incorporate the operators suggested into the evolutionary algorithms NSGAII, MOEA/D and GWASF-GA and we analyse their performances for a data set from the Spanish stock market. The potential of our operators is shown in comparison to other commonly used genetic operators and some conclusions are highlighted from the analysis of the trade-offs among the three criteria.     

蚁群算法优化模糊规则

模糊控制器设计的关键是根据专家经验确定模糊规则。然而,在专家经验难以获取的情况下将无法进行设计,这就要求模糊规则能够自动优化。模糊规则的优化过程为前件选择后件的过程,是一个组合优化问题,本文应用蚁群算法对其进行优化。蚁群算法是一种新型的模拟进化算法,已被广泛且有效的应用到求解复杂的组合优化问题中。仿真结果显示了蚁群算法应用于优化模糊规则的可行性和有效性,扩大了蚁群算法的应用范围,也为模糊控制器的设计提供了新的思路。     

An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms

Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure.     

Cloud detection in satellite images using multi-objective social spider optimization

Cloud detection algorithms have emerged to automate image data analysis because of its prime influential factor in remote sensing image quality. Cloud detection algorithm still needs domain-expert intervention and large number of training examples to ensure good performance whose acquirement becomes difficult due to unavailability of labeled data as well as the time and process heads involved. The paper puts forward multi-objective social spider optimization (MOSSO) based efficient clustering technique to detect clouds in the visible range. This paper explains the proposed MOSSO algorithm along-with the analysis carried on 14 benchmark two-objective test problems against MOEA/D, MODE, MOPSO and SPEA2 multi-objective algorithms. Further, the strengths and weaknesses of the proposed algorithm are analyzed and have been used for the implementation of an efficient clustering technique named as MOSSO-C. Optimal centroid matrix for clustering is attained in MOSSO-C through environmental selection whose performance evaluation has been done on six synthetic databases and are compared with above mentioned conventional multi-objective algorithms. The obtained results encourage the use of MOSSO-C technique to get labeled data for training process of neural network classifier. This approach efficiently classifies the cloudy pixels against various Earth’s surfaces (water, vegetation and land). The paper also discusses the performance evaluation of proposed technique on four Landsat 8 data which shows on an average 96.37% performance accuracy in detecting cloudy pixels.     

Selecting fuzzy if-then rules for classification problems usinggenetic algorithms

This paper proposes a genetic-algorithm-based method for selecting a small number of significant fuzzy if-then rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy if-then rules. Genetic algorithms are applied to this problem. A set of fuzzy if-then rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the combinatorial optimization problem. The performance of the proposed method for training data and test data is examined by computer simulations on the iris data of Fisher     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

Robust multi-objective optimization for water distribution system design using a meta-metaheuristic

The design of an urban water distribution system (WDS) is a challenging problem involving multiple objectives. The goal of robust multi-objective optimization for WDS design is to find the set of solutions which embodies an acceptable trade-off between system cost and reliability, so that the ideal solution may be selected for a given budget. In addition to satisfying consumer needs, a system must be built to accommodate multiple demand loading conditions, withstand component failures and allow surplus capacity for growth. In a developmental setting, WDS robustness becomes even more crucial, owing to the limited availability of resources, especially for maintenance. Recent optimization studies have achieved success using multi-objective evolutionary algorithms, such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II). However, the multi-objective design of a large WDS within a reasonable timeframe remains a formidable problem, owing to the extremely high computational complexity of the problem. In this paper, a meta-algorithm called AMALGAM is applied for the first time to WDS design. AMALGAM uses multiple metaheuristics simultaneously in an attempt to improve optimization performance. Additionally, a Jumping-gene Genetic Algorithm (NSGA-II-JG) is also applied for the first time to WDS design. These two algorithms were tested against some other metaheuristics (including NSGA-II and a new greedy algorithm) with respect to a number of benchmark systems documented in the literature, and AMALGAM demonstrated the best performance overall, while NSGA-II-JG fared worse than the ordinary NSGA-II. Large cost savings and reliability improvements are demonstrated for a real WDS developmental case study in South Africa.     

Design of particle-reinforced polyurethane mould materials for soft tooling process using evolutionary multi-objective optimization algorithms

Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyurethane to make composite mould material. However, addition of fillers affects various properties of the ST process, such as stiffness of the mould box, rendering flow-ability of melt mould material, etc. In the present work, multi-objective optimization of various conflicting objectives (namely maximization of equivalent thermal conductivity, minimization of effective modulus of elasticity, and minimization of equivalent viscosity) of composite material are conducted using evolutionary algorithms (EAs) in order to design particle-reinforced polyurethane composites by finding the optimal values of design parameters. The design parameters include volume fraction of filler content, size and shape factor of filler particle, etc. The Pareto-optimal front is targeted by solving the corresponding multi-objective problem using the NSGA-II procedure. Then, suitable multi-criterion decision-making techniques are employed to select one or a small set of the optimal solution(s) of design parameter(s) based on the higher level information of the ST process for industrial applications. Finally, the experimental study with a typical real industrial application demonstrates that the obtained optimal design parameters significantly reduce the cooling time in soft tooling process keeping other processing advantages.