Simulated annealing based classifier ensemble techniques: Application to part of speech tagging

Part-of-Speech (PoS) tagging is an important pipelined module for almost all Natural Language Processing (NLP) application areas. In this paper we formulate PoS tagging within the frameworks of single and multi-objective optimization techniques. At the very first step we propose a classifier ensemble technique for PoS tagging using the concept of single objective optimization (SOO) that exploits the search capability of simulated annealing (SA). Thereafter we devise a method based on multiobjective optimization (MOO) to solve the same problem, and for this a recently developed multiobjective simulated annealing based technique, AMOSA, is used. The characteristic features of AMOSA are its concepts of the amount of domination and archive in simulated annealing, and situation specific acceptance probabilities. We use Conditional Random Field (CRF) and Support Vector Machine (SVM) as the underlying classification methods that make use of a diverse set of features, mostly based on local contexts and orthographic constructs. We evaluate our proposed approaches for two Indian languages, namely Bengali and Hindi. Evaluation results of the single objective version shows the overall accuracy of 88.92% for Bengali and 87.67% for Hindi. The MOO based ensemble yields the overall accuracies of 90.45% and 89.88% for Bengali and Hindi, respectively.     

C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

In recent years, evolutionary algorithms (EAs) have been extensively developed and utilized to solve multi-objective optimization problems. However, some previous studies have shown that for certain problems, an approach which allows for non-greedy or uphill moves (unlike EAs), can be more beneficial. One such approach is simulated annealing (SA). SA is a proven heuristic for solving numerical optimization problems. But owing to its point-to-point nature of search, limited efforts has been made to explore its potential for solving multi-objective problems. The focus of the presented work is to develop a simulated annealing algorithm for constrained multi-objective problems. The performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems. A comparison with other established multi-objective optimization algorithms, such as infeasibility driven evolutionary algorithm (IDEA), Non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective Scatter search II (MOSS-II) has been included to highlight the benefits of the proposed approach.     

一种混合自适应多目标Memetic算法

Memetic算法是求解多目标优化问题最有效的方法之一，融合了局部搜索和进化计算，具有较高的全局搜索能力．混合自适应多目标Memetic算法（HAMA）用基于模拟退火的加权法进行局部搜索，采用Pareto法实现交叉和变异，通过扰动增强算法的exploration能力，且进化过程可根据改善率自适应调整，以提高搜索效率并改善算法的鲁棒性．算例测试说明HAMA能产生更接近Pareto前沿且多样性更好的近似集．     

An improved simulated annealing simulation optimization method for discrete parameter stochastic systems

This paper proposes a new heuristic algorithm for the optimization of a performance measure of a simulation model constrained under a discrete decision space. It is a simulated annealing-based simulation optimization method developed to improve the performance of simulated annealing for discrete variable simulation optimization. This is accomplished by basing portions of the search procedure on inferred statistical knowledge of the system instead of using a strict random search. The proposed method is an asynchronous team-type heuristic that adapts techniques from response surface methodology and simulated annealing.Testing of this method is performed on a detailed simulation model of a semi-conductor manufacturing process consisting of over 40 work-stations with a cost minimization objective. The proposed method is able to obtain superior or equivalent solutions to an established simulated annealing method during each run of the testing experiment.     

Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem

In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm.     

多目标优化的一类模拟退火算法

多目标优化是运筹学中的重要研究课题,但迄今仍缺少高效的优化技术。通过对搜索操作和参数的合理设置,提出了一类求解多目标优化问题Pareto最优解的高效模拟退火算法。基于典型算例的数值仿真验证了算法的有效性。     

一种基于模拟退火的多目标Memetic算法

为了改善多目标进化算法的搜索效率，提出了基于模拟退火的多目标Memetic算法．此算法根据Pareto占优关系评价个体适应值，采用模拟退火进行局部搜索，并结合交叉算子和基于网格密度的选择机制改善算法的收敛速度和解的均衡分布．flowshop调度问题算例的仿真结果表明，基于模拟退火的多目标Memetic算法能够产生更接近Pareto前沿的近似集．     

基于新模型的多目标Memetic算法及收敛分析

将多目标函数优化问题转化成单目标约束优化问题.对转化后的问题提出了基于约束主导原理的选择方法,克服了多数方法只使用Pareto优胜关系作为选择策略而没有采用偏好信息这一缺陷;Memetic算法是求解多目标优化问题最有效的方法之一,它融合了局部搜索和进化计算.新的多目标Memetic算法引进C-metric,将模拟退火算法与遗传算法结合起米,改善了全局搜索能力.用概率论的有关知识证明了算法的收敛性.仿真结果表明该方法对不同的试验函数均可求出一组沿着Pareto前沿分布均匀且散布广泛的非劣解.     

Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.     

基于模拟退火的粒子群优化算法

粒子群优化算法是一类简单有效的随机全局优化技术。该文把模拟退火思想引入到具有杂交和高斯变异的粒子群优化算法中,给出了一种基于模拟退火的粒子群优化算法。该算法基本保持了粒子群优化算法简单容易实现的特点,但改善了粒子群优化算法摆脱局部极值点的能力,提高了算法的收敛速度和精度。四个基准测试函数的仿真对比结果表明,该算法不仅增强了全局收敛性,而且收敛速度和精度均优于粒子群优化算法。     

Multi-objective clustered-based routing with coverage control in wireless sensor networks

A wireless sensor network (WSN) generally consists of a large number of inexpensive power constrained sensors that are small in size and communicate over short distances to perform a predefined task. Realizing the full potential of WSN poses many design problems, especially those which involve tradeoffs between multiple conflicting optimization objectives such as coverage preservation and energy conservation. While both energy conservation routing protocols in a cluster-based WSNs and coverage-maintenance problems have been extensively studied in the literature, these two problems have not been integrated in a multi-objective optimization (MOO) manner. This paper employs a recently developed MOO algorithm, the so-called multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve simultaneously the energy conservation and coverage preservation design problems in cluster-based WSNs. The performance of the proposed approach, in terms of network lifetime and coverage is compared with the heuristic LEACH and SEP clustering protocols and with another prominent MOEA, the so-called non-dominated sorting genetic algorithm II (NSGA II). Simulation results reveal that MOEA/D provides a more efficient and reliable behavior over other approaches.     

多维多极值函数优化的和声退火算法

针对多极值实函数优化问题,本文结合和声搜索与模拟退火算法,提出了一种新的搜索算法,即和声退火算法。新算法保留了和声搜索的搜索机理,但对和声搜索中于和声记忆库外的搜索方法用超快速模拟退火算法作了改进,对和声记忆库内新解产生方法也作了相应的调整,从而提高了对多维问题的搜索效率。数值实验结果表明算法对和声搜索有明显的改进,收敛速度更快,跳出局部极值点的能力较强。新算法在解决多维多极值优化问题方面比遗传算法更具效率,值得进一步研究与推广应用。     

An adaptive evolutionary multi-objective approach based on simulated annealing

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.     

Simulation of lifting motions using a novel multi-objective optimization approach

In this study, a novel lifting motion simulation model was developed based on a multi-objective optimization (MOO) approach. Two performance criteria, minimum physical effort and maximum load motion smoothness, were selected to define the multi-objective function in the optimization procedure using a weighted-sum MOO approach. Symmetric lifting motions performed by younger and older adults under varied task conditions were simulated. The results showed that the proposed MOO approach led to up to 18.9% reductions in the prediction errors compared to the single-objective optimization approach. This finding suggests that both minimum physical effort and maximum load motion smoothness play an important role in lifting motion planning. Age-related differences in the mechanisms for planning lifting motions were also investigated. In particular, younger workers tend to rely more on the criterion of minimizing physical effort during lifting motion planning, while maximizing load motion smoothness seems to be the dominant objective for older workers.Relevance to industryLifting tasks are closely associated with occupational low back pain (LBP). In this study, a novel lifting motion simulation model was developed to facilitate the analysis of lifting biomechanics and LBP prevention. Age-related differences in lifting motion planning were discussed for better understanding LBP injury mechanisms during lifting.     

Multi-objective ordinal optimization for simulation optimization problems

Ordinal optimization (OO) has been successfully applied to accelerate the simulation optimization process with single objective by quickly narrowing down the search space. In this paper, we extend the OO techniques to address multi-objective simulation optimization problems by using the concept of Pareto optimality. We call this technique the multi-objective OO (MOO). To define the good enough set and the selected set, we introduce two performance indices based on the non-dominance relationship among the designs. Then we derive several lower bounds for the alignment probability under various scenarios by using a Bayesian approach. Numerical experiments show that the lower bounds of the alignment probability are valid when they are used to estimate the size of the selected set as well as the expected alignment level. Though the lower bounds are conservative, they have great practical value in terms of narrowing down the search space.     

考虑安全约束的连退生产过程多目标操作优化

针对连退生产过程中经常出现的薄料带钢跑偏问题,建立考虑安全约束的连退生产过程多目标操作优化模型,并针对问题特点提出一种基于分类和多种群竞争协调的多目标进化算法(MOEA-CMCC).在算法中引入具有不同进化策略的多个种群以增强搜索的多样性,并在多种群之间引入竞争机制和信息共享的协调机制以提高算法的鲁棒性;通过对外部档案集中的解进行分类并在类内进行局部搜索,以保证外部档案集的分散性和算法的收敛速度.基于Benchmark问题的实验结果表明,所提出的算法具有较好的收敛性和分散性;对连退操作优化问题的实验结果表明,所提出的算法能够有效求解该问题.     

Simultaneous feature selection and symmetry based clustering using multiobjective framework

In this paper a new framework based on multiobjective optimization (MOO), namely FeaClusMOO, is proposed which is capable of identifying the correct partitioning as well as the most relevant set of features from a data set. A newly developed multiobjective simulated annealing based optimization technique namely archived multiobjective simulated annealing (AMOSA) is used as the background strategy for optimization. Here features and cluster centers are encoded in the form of a string. As the objective functions, two internal cluster validity indices measuring the goodness of the obtained partitioning using Euclidean distance and point symmetry based distance, respectively, and a count on the number of features are utilized. These three objectives are optimized simultaneously using AMOSA in order to detect the appropriate subset of features, appropriate number of clusters as well as the appropriate partitioning. Points are allocated to different clusters using a point symmetry based distance. Mutation changes the feature combination as well as the set of cluster centers. Since AMOSA, like any other MOO technique, provides a set of solutions on the final Pareto front, a technique based on the concept of semi-supervised classification is developed to select a solution from the given set. The effectiveness of the proposed FeaClustMOO in comparison with other clustering techniques like its Euclidean distance based version where Euclidean distance is used for cluster assignment, a genetic algorithm based automatic clustering technique (VGAPS-clustering) using point symmetry based distance with all the features, K-means clustering technique with all features is shown for seven higher dimensional data sets obtained from real-life.     

Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients

In this article we describe a novel Particle Swarm Optimization (PSO) approach to multi-objective optimization (MOO), called Time Variant Multi-Objective Particle Swarm Optimization (TV-MOPSO). TV-MOPSO is made adaptive in nature by allowing its vital parameters (viz., inertia weight and acceleration coefficients) to change with iterations. This adaptiveness helps the algorithm to explore the search space more efficiently. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. TV-MOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for 11 function optimization problems, using different performance measures.     

面向多目标优化的适应度共享免疫克隆算法

多目标优化的日标在于使得解集能够快速的逼近真实Pareto前沿.针对解的分布性问题,以免疫克隆算法为框架,引入适应度共享策略,提出了一种新的具有良好分布性保持的多目标优化进化算法;算法建立外部群体以保存非支配解,以Pareto优和共亨适应度作为外部群体更新与激活抗体选择的双重标准.为了增强算法对决策空间的开发能力,引入...     

Nonconvex and discrete optimal design using multiple state simulated annealing

New strategies used in multiple state simulated annealing are proposed with the goal of increasing the chances of locating more optima through the use of interactive search strategies. A multiple state simulated annealing is characterized as one in which multiple sequences of state changes, instead of only one, are independently created under a common temperature dropping schedule and state change process. A number of interactive strategies are proposed to interconnect the development of multiple states during the annealing process so that in a single run of miltiple state simulated annealing the design space could be explored more thoroughly and more global/local optima could be discovered. Two illustrative examples including nonconvex and discrete optimization problems are included.