Multi-objective Simulated Annealing Approach Research Articles

Sample classification by selecting informative genes: a greedy multi-objective simulated annealing approach

Sample classification by selecting informative genes: a greedy multi-objective simulated annealing approach

Multi Objective Simulated Annealing Approach for Facility Layout Design

Facility layout design problem considers the departments’ physcial layout design with area requirements in some restrictions such as material handling costs, remoteness and distance requests. Briefly, facility layout problem related to optimization of the layout costs and working conditions. This paper proposes a new multi objective simulated annealing algorithm for solving of the unequal area in layout design. Using of the different objective weights are generated with entropy approach and used in the alternative layout design. Multi objective function takes into the objective function and constraints. The suggested heuristic algorithm used the multi-objective parameters for initialization. Then prefered the entropy approach determines the weight of the objective functions. After the suggested improved simulated annealing approach applied to whole developed model. A multi-objective simulated annealing algorithm is implemented to increase the diversity and reduce the chance of getting layout conditions in local optima.

Harnessing multi-objective simulated annealing toward configuration optimization within compact space for additive manufacturing

Abstract The rapid advancement of additive manufacturing technology has led to new opportunities and challenges. One potential advantage of additive manufacturing is the possibility of producing systems with reduced volumes/weights. This research concerns a type of configuration optimization problems, where the envelope volume in space occupied by a number of components is to be minimized along with other objectives. Since in practical applications the objectives and constraints are usually complex, the formulation of computationally tractable optimization becomes difficult. Moreover, unlike conventional multi-objective problems, these configuration optimization problems usually come with a number of demanding constraints that are hard to satisfy, which results in the critical challenge of balancing solution feasibility with optimality. In this research, the mathematical formulation of a representative problem of configuration optimization with multiple hard constraints is presented first, followed by two newly developed versions of an enhanced multi-objective simulated annealing approach, referred to as MOSA/R, to solve this challenging problem. To facilitate the optimization computationally, in MOSA/R, a versatile re-seed scheme allowing biased search while avoiding pre-mature convergence is designed. Re-seed can generally lead to more comprehensive search in the parametric space. Case studies indicate that the new algorithm yields significantly improved performance towards both constrained benchmark tests and constrained configuration optimization problem. The methodology developed can lead to an integrated framework of design and additive manufacturing.

Tradeoffs between levelling the reserve margin and minimising production cost in generator maintenance scheduling for regulated power systems

One of the key focus areas for a power utility is the planned preventative maintenance of the power generating units in its power system. The well-known generator maintenance scheduling (GMS) problem involves finding a schedule for the planned maintenance outages of generating units in a power system. A novel bi-objective model is proposed for the GMS problem in which demand reliability is maximised, by minimising the sum of squared reserves (SSR), and electricity production cost (predominantly fuel cost) is minimised. A novel production planning module is proposed to estimate the production cost associated with an energy generation plan, using a linear programming (LP) model to solve the economic dispatch (ED) problem, which precedes application of a simple unit commitment (UC) algorithm. A dominance-based multi-objective simulated annealing approach is then adopted to determine trade-off solutions to the model. Parallel computing is also utilised to increase the efficiency of approximating the Pareto front. The modelling approach is demonstrated in the context of a case study involving the 32-unit IEEE Reliability Test System. The results are compared to the best known single-objective solution in the literature, which only minimises the SSR, and the conflicting relationship between the two model objectives is investigated. It is found that more non-dominated trade-off solutions result if the load demand increases (i.e.the gap between installed capacity and load demand decreases). Therefore, if the installed capacity is sufficiently high, the reliability objective of minimising the SSR produces sufficiently small production cost solutions. Fuel cost savings of 0.41% are achieved in respect of a most “reliable” solution in the literature, but considerable cost savings are possible (up to 7.11%) if the maintenance duration and crew constraints are relaxed.

A Bi-Objective RNN Model to Reconstruct Gene Regulatory Network: A Modified Multi-Objective Simulated Annealing Approach.

Gene Regulatory Network (GRN) is a virtual network in a cellular context of an organism, comprising a set of genes and their internal relationships to regulate protein production rate (gene expression level) of each other through coded proteins. Computational Reconstruction of GRN from gene expression data is a widely-applied research area. Recurrent Neural Network (RNN) is a useful modeling scheme for GRN reconstruction. In this research, the RNN formulation of GRN reconstruction having single objective function has been modified to incorporate a new objective function. An existing multi-objective meta-heuristic algorithm, called Archived Multi Objective Simulated Annealing (AMOSA), has been modified and applied to this bi-objective RNN formulation. Executing the resulting algorithm (called AMOSA-GRN) on a gene expression dataset, a collection (termed as Archive) of non-dominated GRNs has been obtained. Ensemble averaging has been applied on the archives, and obtained through a sequence of executions of AMOSA-GRN. Accuracy of GRNs in the averaged archive, with respect to gold standard GRN, varies in the range 0.875 - 1.0 (87.5 - 100 percent).

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.

Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition

In this paper, we propose a two-stage multiobjective-simulated annealing (MOSA)-based technique for named entity recognition (NER). At first, MOSA is used for feature selection under two statistical classifiers, viz. conditional random field (CRF) and support vector machine (SVM). Each solution on the final Pareto optimal front provides a different classifier. These classifiers are then combined together by using a new classifier ensemble technique based on MOSA. Several different versions of the objective functions are exploited. We hypothesize that the reliability of prediction of each classifier differs among the various output classes. Thus, in an ensemble system, it is necessary to find out the appropriate weight of vote for each output class in each classifier. We propose a MOSA-based technique to determine the weights for votes automatically. The proposed two-stage technique is evaluated for NER in Bengali, a resource-poor language, as well as for English. Evaluation results yield the highest recall, precision and F-measure values of 93.95, 95.15 and 94.55 %, respectively for Bengali and 89.01, 89.35 and 89.18 %, respectively for English. Experiments also suggest that the classifier ensemble identified by the proposed MOO-based approach optimizing the F-measure values of named entity (NE) boundary detection outperforms all the individual classifiers and four conventional baseline models.

A multi-objective simulated annealing approach to reactive power compensation

Reactive power compensation is an important problem in electrical distribution systems, involving the sizing and location of capacitors (sources of reactive power). The installation of capacitors also contributes to releasing system capacity and improving voltage level. A multi-objective simulated annealing approach to provide decision support in this problem is presented. This approach is able to compute a set of well-distributed and diversified solutions underlying distinct trade-offs, even for a challenging network. The characterization of the non-dominated front is relevant information for aiding planning engineers to select satisfactory compromise solutions (compensation schemes) to improve the network operation conditions.

A multiobjective simulated annealing approach for classifier ensemble: Named entity recognition in Indian languages as case studies

In this paper, we propose a simulated annealing (SA) based multiobjective optimization (MOO) approach for classifier ensemble. Several different versions of the objective functions are exploited. We hypothesize that the reliability of prediction of each classifier differs among the various output classes. Thus, in an ensemble system, it is necessary to find out the appropriate weight of vote for each output class in each classifier. Diverse classification methods such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM) are used to build different models depending upon the various representations of the available features. One most important characteristics of our system is that the features are selected and developed mostly without using any deep domain knowledge and/or language dependent resources. The proposed technique is evaluated for Named Entity Recognition (NER) in three resource-poor Indian languages, namely Bengali, Hindi and Telugu. Evaluation results yield the recall, precision and F-measure values of 93.95%, 95.15% and 94.55%, respectively for Bengali, 93.35%, 92.25% and 92.80%, respectively for Hindi and 84.02%, 96.56% and 89.85%, respectively for Telugu. Experiments also suggest that the classifier ensemble identified by the proposed MOO based approach optimizing the F-measure values of named entity (NE) boundary detection outperforms all the individual models, two conventional baseline models and three other MOO based ensembles.

Evolutionary Rough Parallel Multi-Objective Optimization Algorithm

A hybrid unsupervised learning algorithm, which is termed as Parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation, deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competencemeasurement, the time-efficient parallel approach enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based dynamic archive classifi- cation method and the distributed implementation as a two-phase speedup strategy in this algorithm. A measure of the amount of domination between two solutions has been incorporated in this work to determine the acceptance probability of a new solution with an improvement in the spread of the non-dominated solutions in the Pareto-front by adopting rough sets theory. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with three other existing and well-known Multi-Objective Evolutionary Algorithms (MOEAs) demonstrate the effectiveness of the former with respect to four existing performance metrics and eleven benchmark test problems of varying degrees of difficulties. The superiority of this new parallel implementation over other algorithms also has been demonstrated in timing, which achieves a near optimal speedup with a minimal communication overhead.

A multi-objective production scheduling case study solved by simulated annealing

During several decades, research in production scheduling mainly concerns a single criterion to optimize. However, the analysis of the performance of a schedule often involves more than one aspect and therefore requires multi-objective analysis. Such situation appears in the real case study considered here. This paper deals with a production scheduling problem in a flexible (or hybrid) job-shop with particular constraints: batch production; existence of two steps: production of several sub-products followed by the assembly of the final product; possible overlaps for the processing periods of two successive operations of a same job. At the end of the production step, different objectives should be considered simultaneously, among the makespan, the mean completion time, the maximal tardiness, the mean tardiness. The research is based on a real case study, concerning a Tunisian firm. We propose a multi-objective simulated annealing approach to tackle this problem and to propose to the manager an approximation of the set of efficient schedules. Several numerical results are reported.