Metaheuristic downhill simplex method in combinatorial optimization

The concept of combinatorial objects is formalized. It allows strict definition of a combinatorial optimization problem (COP). An efficient metaheuristic method to solve COPs (H-method) is considered. It includes stochastic local search algorithms as a built-in procedure. A parallel implementation of the H-method is set forth and analyzed. The results from a numerical experiment and solution of some well-known COPs on personal computers and on the SKIT cluster supercomputer are presented. The study was supported by INTAS (Project 06-1000017-8909). __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 70–79, November–December 2007.     

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system, the matching prediction and similarity identification take place a major role in the ontology. In that, there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time. Since, in the data recommendation system, this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process. To improve the performance of data validation, this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing. In this paper advanced model of the Logarithmic Directionality Texture Pattern (LDTP) method with a Metaheuristic Pattern Searching (MPS) system was used to estimate the similarity between the query data in the entire database. The overall work was implemented for the application of the data recommendation process. These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period. Also, with the help of a neural network, the relevancies of feature attributes in the database are predicted, and the matching index was sorted to provide the recommended data for given query data. This was achieved by using the Distributional Recurrent Neural Network (DRNN). This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set. The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset. These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric. The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%, 70%, and 80%. The parameters that are considered for performance analysis are Precision, Recall, F1-score and the accuracy of data retrieval, the query recommendation output, and comparison with other state-of-art methods.     

An improved harmony search algorithm with dynamically varying bandwidth

The present work demonstrates a new variant of the harmony search (HS) algorithm where bandwidth (BW) is one of the deciding factors for the time complexity and the performance of the algorithm. The BW needs to have both explorative and exploitative characteristics. The ideology is to use a large BW to search in the full domain and to adjust the BW dynamically closer to the optimal solution. After trying a series of approaches, a methodology inspired by the functioning of a low-pass filter showed satisfactory results. This approach was implemented in the self-adaptive improved harmony search (SIHS) algorithm and tested on several benchmark functions. Compared to the existing HS algorithm and its variants, SIHS showed better performance on most of the test functions. Thereafter, the algorithm was applied to geometric parameter optimization of a friction stir welding tool.     

Algorithms for a realistic variant of flowshop scheduling

This paper deals with a realistic variant of flowshop scheduling, namely the hybrid flexible flowshop. A hybrid flowshop mixes the characteristics of regular flowshops and parallel machine problems by considering stages with parallel machines instead of having one single machine per stage. We also investigate the flexible version where stage skipping might occur, i.e., not all stages must be visited by all jobs. Lastly, we also consider job sequence dependent setup times per stage. The optimization criterion considered is makespan minimization. While many approaches for hybrid flowshops have been proposed, hybrid flexible flowshops have been rarely studied. The situation is even worse with the addition of sequence dependent setups. In this study, we propose two advanced algorithms that specifically deal with the flexible and setup characteristics of this problem. The first algorithm is a dynamic dispatching rule heuristic, and the second is an iterated local search metaheuristic. The proposed algorithms are evaluated by comparison against seven other high performing existing algorithms. The statistically sound results support the idea that the proposed algorithms are very competitive for the studied problem.     

A procedure for the generation of interval type-2 membership functions from data

This paper proposes a new interval type-2 fuzzy set taking extended π interval type-2 membership function (IT2 MF) as its values, and presents a new procedure for generating a set of extended π IT2 MFs from data for an interval type-2 linguistic variable. An extended π IT2 MF is defined as the min and max of two extended π (type-1 or ordinary) membership functions. The procedure has the following steps: (i) for each interval type-2 linguistic variable, specifying the number of membership functions to be generated, i.e. the granularity level, (ii) choosing two fuzzy exponents to be used, (iii) for each fuzzy exponent, applying the fuzzy c-means variant (FCMV) proposed by Liao et al. [1] to obtain the corresponding centers and membership values, and (iv) carrying out parametric optimization by applying a metaheuristic or a hybrid metaheuristic algorithm to determine the optimal parameters associated with the extended π IT2 MFs so that the mean squared error (MSE) or sum of squared errors (SSE) between the membership values obtained by FCMV and those predicted by the extended π IT2 MFs is minimized. The proposed procedure was illustrated with an example and further tested with iris data and weld data. The effects of using two different interval distance measures and the cluster means obtained by the FCMV as part of the initial solutions in the differential evolution metaheuristic were also investigated and discussed.     

A stochastic local search algorithm for constrained continuous global optimization

This paper presents a new stochastic local search algorithm known as feasible–infeasible search procedure (FISP) for constrained continuous global optimization. The proposed procedure uses metaheuristic strategies for combinatorial optimization as well as combined strategies for exploring continuous spaces, which are applied to an efficient process in increasingly refined neighborhoods of current points. We show effectiveness and efficiency of the proposed procedure on a standard set of 13 well‐known test problems. Furthermore, we compare the performance of FISP with SNOPT (sparse nonlinear optimizer) and with few successful existing stochastic algorithms on the same set of test problems.     

元启发搜索技术在测试数据产生中应用研究

提出一种使用元启发搜索技术产生测试数据的方法。这种方法能够针对测试中低概率执行部分产生测试数据，提高测试覆盖率，并通过实验举例说明了该方法的可行性。     

A comparison of five hybrid metaheuristic algorithms for unrelated parallel-machine scheduling and inbound trucks sequencing in multi-door cross docking systems

The objective of this paper is to develop five hybrid metaheuristic algorithms, including three hybrid ant colony optimization (hACO) variants, and compare their performances in two related applications: unrelated parallel machine scheduling and inbound truck sequencing in a multi-door cross docking system in consideration of sequence dependent setup, and both zero and nonzero release time. The three hACO variants were modified and adapted from the existing literature and they differ mainly in how a solution is coded and decoded, how a pheromone matrix is represented, and the local search methods employed. The other two hybrids are newly constructed hybrid simulated annealing (hSA) algorithms, which are built based on the authors’ knowledge and experience. The evaluation criteria are computational time and the objective function value, i.e., makespan. Based on the results of computational experiments the simulated annealing-tabu search hybrid turns out to be the best if maximal CPU time is used as the stopping criterion and the 2-stage hACO variant is the best if maximal number of evaluations is the stopping criterion. The contributions of this paper are: (i) being the first to carry out a comparative study of hybrid metaheuristics for the two selected applications, (ii) being the first to consider nonzero truck arrival time in multi-door cross docking operations, (iii) identifying which hACO variant is the best among the three, and (iv) investigating the effect of release time on the makespan.     

Ride sharing with flexible participants: a metaheuristic approach for large‐scale problems

Severe traffic congestion and growing ecological consciousness have led to the rise of alternative transportation systems. Ride sharing is one such alternative in which drivers and passengers with similar time schedules and travel plans are matched. For this service to be effective, a large number of users are required to increase the probability of finding suitable travel partners. The present paper proposes a late acceptance metaheuristic to decide which users act as drivers and to construct their routes. The underlying optimization model allows passengers to walk to/from alternative pickup/drop‐off locations so as to further exploit user flexibility. A computational study quantifies the impact of different types of participant flexibility on CO₂ emissions. These insights can inform and support policymakers in organizing effective ride‐sharing systems.     

Cyclical parthenogenesis algorithm for layout optimization of truss structures with frequency constraints

Structural optimization with frequency constraints is seen as a challenging problem because it is associated with highly nonlinear, discontinuous and non-convex search spaces consisting of several local optima. Therefore, competent optimization algorithms are essential for addressing these problems. In this article, a newly developed metaheuristic method called the cyclical parthenogenesis algorithm (CPA) is used for layout optimization of truss structures subjected to frequency constraints. CPA is a nature-inspired, population-based metaheuristic algorithm, which imitates the reproductive and social behaviour of some animal species such as aphids, which alternate between sexual and asexual reproduction. The efficiency of the CPA is validated using four numerical examples.