一种求解鲁棒优化问题的多目标进化方法

鲁棒优化问题（Robust Optimization Problem,ROP）是进化算法（Evolutionary Algorithms,EAs）研究的重要方面之一,对于许多实际工程优化问题,通常需要得到鲁棒最优解。利用多目标优化中的Pareto思想优化ROP的鲁棒性和最优性,将ROP转化为一个两目标的优化问题,一个目标为解的鲁棒性,一个目标为解的最优性。针对ROP与多目标优化的特点,利用动态加权思想,设计一种求解ROP的多目标进化算法。通过测试函数的实验仿真,验证了该方法的有效性。     

多目标遗传算法求解认知无线电性能优化问题

认知无线电的性能优化是一个动态多目标优化问题。现有的Bio-CR模型基于遗传算法优化认知无线电的性能,它使用线性加权方法将此多目标优化问题简化为了一个单目标优化问题。针对Bio-CR很难确定每个适应度函数的权值和容易漏掉一些最优解的问题,提出了基于多目标遗传算法的认知无线电性能优化算法CREA。CREA能够根据信道条件和用户服务需求的变化动态地调整传输参数以优化性能,不仅克服了Bio-CR的两个缺点,而且通过保存计算结果进一步减少了遗传算法的运行次数。CREA首先根据信道条件的变化动态确定一组适应度函数,然后运行多目标遗传算法获得一个Pareto-optimal set,最后根据用户服务需求从中选出一个最满意解,并通知认知无线电更新自己的传输参数。Matlab仿真实验证明了CREA的正确性和有效性。     

Fuzzy portfolio selection using genetic algorithm

This paper presents the development of fuzzy portfolio selection model in investment. Fuzzy logic is utilized in the estimation of expected return and risk. Using fuzzy logic, managers can extract useful information and estimate expected return by using not only statistical data, but also economical and financial behaviors of the companies and their business strategies. In the formulated fuzzy portfolio model, fuzzy set theory provides the possibility of trade-off between risk and return. This is obtained by assigning a satisfaction degree between criteria and constraints. Using the formulated fuzzy portfolio model, a Genetic Algorithm (GA) is applied to find optimal values of risky securities. Numerical examples are given to demonstrate the effectiveness of proposed method.     

Improved backtracking search algorithm for pseudo dynamic active earth pressure on retaining wall supporting c-Ф backfill

Optimization algorithms are effective and powerful tools for solving the non-linear optimization problems. Backtracking Search Optimization Algorithm (BSA) is a newly proposed Evolutionary Algorithm (EA) and has been applied to optimize different complex optimization problems in science and engineering. In the present study, a new adaptive control parameter based Improved Backtracking Search Optimization Algorithm (IBSA) is suggested. Due to the validation of the suggested method, it has been applied to CEC2005 benchmark functions and the simulation results are compared with different existing algorithms. Also, it has been used to determine active earth pressure on retaining wall supporting c-Ф backfill using the pseudo dynamic method. Simulation result shows that the proposed method is suitable to solve such type of problems and the results obtained are found satisfactory.     

Efficient protocol for data clustering by fuzzy Cuckoo Optimization Algorithm

Data clustering is a technique for grouping similar and dissimilar data. Many clustering algorithms fail when dealing with multi-dimensional data. This paper introduces efficient methods for data clustering by Cuckoo Optimization Algorithm; called COAC and Fuzzy Cuckoo Optimization Algorithm, called FCOAC. The COA by inspire of cuckoo bird nature life tries to solve continuous problems. This algorithm clusters a large dataset to prior determined clusters numbers by this meta-heuristic algorithm and optimal the results by fuzzy logic. Firstly, the algorithm generates a random solutions equal to cuckoo population and with length dataset objects and with a cost function calculates the cost of each solution. Finally, fuzzy logic tries for the optimal solution. The performance of our algorithm is evaluated and compared with COAC, Black hole, CS, K-mean, PSO and GSA. The results show that our algorithm has better performance in comparison with them.     

A novel fuzzy facial expression recognition system based on facial feature extraction from color face images

Emotion recognition plays an effective and important role in Human-Computer Interaction (HCI). Recently, various approaches to emotion recognition have been proposed in the literature, but they do not provide a powerful approach to recognize emotions from Partially Occluded Facial Images.In this paper, we propose a new method for Emotion Recognition from Facial Expression using Fuzzy Inference System (FIS). This novel method is even able to recognize emotions from Partially Occluded Facial Images. Moreover, this research describes new algorithms for facial feature extraction that demonstrate satisfactory performance and precision. In addition, one of the main factors that have an important influence on the final precision of fuzzy inference systems is the membership function parameters. Therefore, we use a Genetic Algorithm for parameter-tuning of the membership functions. Experimental results report an average precision rate of 93.96% for Emotion Recognition of six basic emotions, which is so promising.     

Quantum seeded evolutionary computational technique for constrained optimization in engineering design and manufacturing

In this paper an attempt is made to develop a new Quantum Seeded Hybrid Evolutionary Computational Technique (QSHECT) that is general, flexible and efficient in solving single objective constrained optimization problems. It generates initial parents using quantum seeds. It is here that QSHECT incorporates ideas from the principles of quantum computation and integrates them in the current framework of Real Coded Evolutionary Algorithm (RCEA). It also incorporates Simulated Annealing (SA) in the selection process of Evolutionary Algorithm (EA) for child generation. The proposed algorithm has been tested on standard test problems and engineering design problems taken from the literature. In order to test this algorithm on domain-specific manufacturing problems, Neuro-Fuzzy (NF) modeling of hot extrusion is attempted and the NF model is incorporated as a fitness evaluator inside the QSHECT to form a new variant of this technique, i.e. Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Computational Technique (QSNFHECT) and is effectively applied for process optimization of hot extrusion process. The neuro-fuzzy model (NF) is also compared with statistical regression analysis (RA) model for evaluating the extrusion load. The NF model was found to be much superior. The optimal process parameters obtained by Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Technique (QSNFHECT) are validated by the finite element model. The proposed methodology using QSNFHECT is a step towards meeting the challenges posed in intelligent manufacturing systems and opens new avenues for parameter estimation and optimization and can be easily incorporated in existing manufacturing setup.     

The effects of asymmetric neighborhood assignment in the MOEA/D algorithm

The Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D) is a very efficient multiobjective evolutionary algorithm introduced in recent years. This algorithm works by decomposing a multiobjective optimization problem to many scalar optimization problems and by assigning each specimen in the population to a specific subproblem. The MOEA/D algorithm transfers information between specimens assigned to the subproblems using a neighborhood relation.In this paper it is shown that parameter settings commonly used in the literature cause an asymmetric neighbor assignment which in turn affects the selective pressure and consequently causes the population to converge asymmetrically. The paper contains theoretical explanation of how this bias is caused as well as an experimental verification. The described effect is undesirable, because a multiobjective optimizer should not introduce asymmetries not present in the optimization problem. The paper gives some guidelines on how to avoid such artificial asymmetries.     

A powerful hybrid clustering method based on modified stem cells and Fuzzy C-means algorithms

One of the simple techniques for Data Clustering is based on Fuzzy C-means (FCM) clustering which describes the belongingness of each data to a cluster by a fuzzy membership function instead of a crisp value. However, the results of fuzzy clustering depend highly on the initial state selection and there is also a high risk for getting the best results when the datasets are large. In this paper, we present a hybrid algorithm based on FCM and modified stem cells algorithms, we called it SC-FCM algorithm, for optimum clustering of a dataset into K clusters. The experimental results obtained by using the new algorithm on different well-known datasets compared with those obtained by K-means algorithm, FCM, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) Algorithm demonstrate the better performance of the new algorithm.     

A robust fuzzy multi-objective location-routing problem for hazardous waste under uncertain conditions

Industrialization and population growth have been accompanied by many problems such as waste management worldwide. Waste management and reduction have a vital role in national management. The presents study represents a multi-objective location-routing problem for hazardous wastes. The model was solved using Non dominated Sorting Genetic Algorithm-II, Multi-Objective Particle Swarm Optimization, Multi-Objective Invasive Weed Optimization, Pareto Envelope-based Selection Algorithm, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer algorithms. The findings revealed that the Multi-Objective Invasive Weed Optimization algorithm was the best and the most efficient among the algorithms used in this study. Obtaining income from the incineration of the wastes and reducing the risk of COVID-19 infection are the first innovation of the present study, which considered in the presented model. The second innovation is that uncertainty was considered for some of the crucial parameters of the model while the robust fuzzy optimization model was applied. Besides, the model was solved using several meta-heuristic algorithms such as Multi-Objective Invasive Weed Optimization, Multi-Objective Evolutionary Algorithm Based on Decomposition and Multi-Objective Grey Wolf Optimizer, which were rarely used in literature. Eventually, the most efficient algorithm was identified by comparing the considered algorithms.

     

Participatory Framework for Assessment and Improvement of Tools (ParFAIT): Increasing the impact and relevance of water management decision support research

This paper proposes the Participatory Framework for Assessment and Improvement of Tools (ParFAIT) as a way to address low uptake of Water Resources Systems Optimization (WRSO) tools. ParFAIT is a transdisciplinary process conducted in five stages, two of which are participatory modeling (PM) exercises. Herein we describe the framework, introduce our candidate tool- Multiobjective Evolutionary Algorithm (MOEA)-assisted optimization, and present the results of our first PM workshop. MOEA-assisted optimization has been put forth as a planning and decision making aid for utilities facing a large number of decisions and highly uncertain futures. The PM workshop, designed to solicit input on a tool testbed, was held in February 2015 with representatives from six Front Range, Colorado, water utilities. Our results include an expanded characterization of the decision making landscape, feedback on water utility decisions and performance goals commonly employed in WRSO studies, and new questions that warrant future investigation by researchers.     

Project scheduling under uncertainty using fuzzy modelling and solving techniques

In the real world, projects are subject to numerous uncertainties at different levels of planning. Fuzzy project scheduling is one of the approaches that deal with uncertainties in project scheduling problem. In this paper, we provide a new technique that keeps uncertainty at all steps of the modelling and solving procedure by considering a fuzzy modelling of the workload inspired from the fuzzy/possibilistic approach. Based on this modelling, two project scheduling techniques, Resource Constrained Scheduling and Resource Leveling, are considered and generalized to handle fuzzy parameters. We refer to these problems as the Fuzzy Resource Constrained Project Scheduling Problem (FRCPSP) and the Fuzzy Resource Leveling Problem (FRLP). A Greedy Algorithm and a Genetic Algorithm are provided to solve FRCPSP and FRLP respectively, and are applied to civil helicopter maintenance within the framework of a French industrial project called Helimaintenance.     

Applying MOEAs to solve the static Routing and Wavelength Assignment problem in optical WDM networks

Wavelength-Division Multiplexing (WDM) in optical networks has revolutionized the Telecommunication field. This technology is able to exploit the enormous bandwidth capability of this kind of networks, allowing communication between end users via all-optical WDM channels (lightpath). Given a set of demands, the problem of setting up lightpaths by routing and assigning a wavelength to each connection is known as Routing and Wavelength Assignment (RWA) problem. There are two types of connection demands: static (demands are given in advance) and dynamic (demands are given in real-time). In this paper we present two different Multiobjective Evolutionary Algorithms (MOEA) with the aim of solving the static RWA problem. The first one is a population-based algorithm, the Differential Evolution (DE), but incorporating the Pareto Tournament concept (DEPT). The second one is a multiobjective version of the Variable Neighborhood Search (VNS), MO-VNS. In order to prove the goodness of our metaheuristics, we have compared them with the standard Fast Non-Dominated Sorting Genetic Algorithm (NSGA-II), typical heuristics in the Telecommunication field, and different varieties of Multiobjective Ant Colony Optimization Algorithms. On the whole, we conclude that our approaches have obtained very promising results.     

Construction of a neuron-fuzzy classification model based on feature-extraction approach

In this paper, a Feature-Extraction Neuron-Fuzzy Classification Model (FENFCM) is proposed that enables the extraction of feature variables and provides the classification results. The proposed classification model synergistically integrates a standard fuzzy inference system and a neural network with supervised learning. The FENFCM automatically generates the fuzzy rules from the numerical data and triangular functions that are used as membership functions both in the feature extraction unit and in the inference unit. To adapt the proposed FENFCM, two modificatory algorithms are applied. First, we utilize Evolutionary Programming (EP) to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the Weight Revised Algorithm (WRA) is used to regulate the weight grade of the principal output node of the inference unit. Finally, the proposed FENFCM is validated using two benchmark data sets: the Wine database and the Iris database. Computer simulation results demonstrate that the proposed classification model can provide a sufficiently high classification rate in comparison with that of other models proposed in the literature.     

A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules

Fuzzy set theory has been used as an approach to deal with uncertainty in the supplier selection decision process. However, most studies limit applications of fuzzy set theory to outranking potential suppliers, not including a qualification stage in the decision process, in which non-compensatory types of decision rules can be used to reduce the set of potential suppliers. This paper presents a supplier selection decision method based on fuzzy inference that integrates both types of approaches: a non-compensatory rule for sorting in qualification stages and a compensatory rule for ranking in the final selection. Fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multicriteria decision making methods. Fuzzy inference combined with a fuzzy rule-based classification method is used to categorize suppliers in qualification stages. Classes of supplier performance can be represented by linguistic terms, which allow decision makers to deal with subjectivity and to express qualification requirements in linguistic formats. Implementation of the proposed method and techniques were analyzed and discussed using an illustrative case. Three defuzzification operators were used in the final selection, yielding the same ranking. Factorial design was applied to test consistency and sensitivity of the inference rules. The findings reinforce the argument that including stages of qualification based on fuzzy inference and categorization makes this method especially useful for selecting from a large set of potential suppliers and also for first time purchase.     

利用神经网络进行推理的模糊控制器

本文介绍了一种利用神经网络进行推理的模糊控制器。网络的输入和输出均为模糊集。训练后的网络能完成合成关系，即模糊推时。为了减少ＢＰ网络的高线训练时间，对模糊集进行了“编码”。最后给出了该控制器应用于曲线环节的实时控制结果。     

模糊寻优问题的模糊遗传算法FGA

针对模糊寻优问题，本文基于模糊集的质心概念来确定模糊集的大小，并进而提出了模糊遗传算法ＦＧＡ。算法ＦＧＡ与遗传算法ＧＡ有本制区别，能用效地找到了模糊寻优问题的近似解，本文对算法ＦＧＡ的有效性作了较深入的分析。     

Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning

This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior.     

An interactive computer graphics interface for the introduction of fuzzy inference in environmental education

Fuzzy logic is based on sets of rules that can be easily understood by the students, since they bear a close resemblance to natural language. The introduction of fuzzy logic, within the framework of Environmental Education, is considered to be necessary in order to provide an insight to the complex environmental interactions. Fuzzy inference is introduced in this paper as an extension of hypothetico-predictive argumentation and it allows the investigation of alternative hypotheses. This is achieved through the development of an interactive computer graphics environment that encompasses a set of fuzzy logic analysis tools and a fuzzy inference model of a lake. The fuzzy model guarantees the scientific integrity of the simulation results, and the graphical interface presents to the students only the comprehensible characteristics of the environmental stressors in the ecosystem of the lake. The proposed graphical interface was developed in successive design stages, with the active participation of the students. The results of the students' experimentation with the graphical interface indicate that their comprehension of the significant environmental problems of the lake is considerably improved and some misconceptions are resolved. Thus, it is considered valuable as an aid to environmental education.     

Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. The study reveals that the African Buffalo Optimization and the Ant Colony Optimization are the best in solving the symmetric TSP, which is similar to intelligence gathering channel in the strategic management of big organizations, while the Randomized Insertion Algorithm holds the best promise in asymmetric TSP instances akin to strategic information exchange channels in strategic management.