Library Of Instances Research Articles

The study on construction and application of knowledge base based on TRIZ and bionics

The TRIZ and bionics are two main methods to guide invention and innovation, which have universal guiding significance to product concept innovation. However, the function of TRIZ is more specific and not conducive to expansion. In this paper, TRIZ function is extended by reclassifying and defining TRIZ function. Then the extended functions and biological functions are combined to build a knowledge base combining TRIZ and bionics. Engineers can use this knowledge base to accurately match imitation sources. It also helps designers improve design efficiency. Finally, the feasibility of the biological instance library is verified by the rib design of the flap-like flaps.

A novel metaheuristic optimization algorithm: the monarchy metaheuristic

In this paper, we introduce a novel metaheuristic optimization algorithm named the monarchy metaheuristic (MN). Our proposed metaheuristic was inspired by the monarchy government system. Unlike many other metaheuristics, it is easy to implement and does not need a lot of parameters. This makes it applicable to a wide range of optimization problems. To evaluate the efficiency of the proposed algorithm, we examined it on the traveling salesman problem (TSP) using some benchmark from TSPLIB online library of instances for the TSP. The experimental results indicate that the monarchy metaheuristic is competitive with the other methods that exist in the literature for finding approximate solutions.

A project scheduling problem with spatial resource constraints and a corresponding guided local search algorithm

Spatial area is a nonlinear resource encountered in many industries. Using the block assembly process in a shipbuilding company as a case study, we propose a project scheduling problem with spatial resource constraints (2D-RCPSP). Due to the two-dimensional properties of spatial resources, the 2D-RCPSP is more difficult to solve than the well-known NP-hard resource constrained project scheduling problem (RCPSP). For the 2D-RCPSP, we develop a sophisticated guided local search algorithm (GLS). The GLS incorporates penalty terms into the cost function and uses it, instead of the original objective function, to guide the search to the promising regions in search space. Furthermore, rapid improvement strategies are investigated in order to increase the efficiency of the local search. To test our GLS for the 2D-RCPSP, we have designed three classes of problem instances, two of which are adapted from the commonly referenced benchmark instance library PSPLIB for RCPSP, whereas the other is derived from real shipyard production data. Three types of tests, each for one class of instances, are implemented. The computational results on these instances as well as the tuning for key parameter in the GLS are presented.

An Improved Augmented <inline-formula> <tex-math notation="LaTeX">$\varepsilon$ </tex-math> </inline-formula>-Constraint and Branch-and-Cut Method to Solve the TSP With Profits

In this paper, a novel approach is presented to solve the traveling salesman problem with profits (TSPP). This problem is an extension of the famous traveling salesman problem (TSP) in which a prize is associated with each vertex. The aim here is to find a route that simultaneously minimizes the tour length and maximizes the collected prize. A new method, AUGMECON2-BC, was developed incorporating an improved version of the augmented e-constraint and a Branch and Cut algorithm. This method transforms a multi-objective problem into a mono-objective problem by optimizing one of the objective functions and converting the others into constraints. The Branch and Cut algorithm is then proposed to solve the generated subproblems. Computational results are taken on TSP Library instances. The performance of this approach is slightly better than what is already found from previous works and it is able to produce the exact Pareto front for the TSPP.

The intermittent travelling salesman problem

AbstractIn this paper, we introduce a new variant of the travelling salesman problem, namely the intermittent travelling salesman problem (ITSP), which is inspired by real‐world drilling/texturing applications. In this problem, each vertex can be visited more than once and there is a temperature constraint enforcing a time lapse between two consecutive visits. A branch‐and‐bound approach is proposed to solve small instances to optimality. We furthermore develop four variable neighbourhood search metaheuristics which produce high‐quality solutions for large instances. An instance library is built and made publicly available.

QPLIB: a library of quadratic programming instances

This paper describes a new instance library for quadratic programming (QP), i.e., the family of continuous and (mixed)-integer optimization problems where the objective function and/or the constraints are quadratic. QP is a very diverse class of problems, comprising sub-classes ranging from trivial to undecidable. This diversity is reflected in the variety of QP solution methods, ranging from entirely combinatorial approaches to completely continuous algorithms, including many methods for which both aspects are fundamental. Selecting a set of instances of QP that is at the same time not overwhelmingly onerous but sufficiently challenging for the different, interested communities is therefore important. We propose a simple taxonomy for QP instances leading to a systematic problem selection mechanism. We then briefly survey the field of QP, giving an overview of theory, methods and solvers. Finally, we describe how the library was put together, and detail its final contents.

A cooperative GPU-based Parallel Multistart Simulated Annealing algorithm for Quadratic Assignment Problem

GPU hardware and CUDA architecture provide a powerful platform to develop parallel algorithms. Implementation of heuristic and metaheuristic algorithms on GPUs are limited in literature. Nowadays developing parallel algorithms on GPU becomes very important. In this paper, NP-Hard Quadratic Assignment Problem (QAP) that is one of the combinatorial optimization problems is discussed. Parallel Multistart Simulated Annealing (PMSA) method is developed with CUDA architecture to solve QAP. An efficient method is developed by providing multistart technique and cooperation between threads. The cooperation is occurred with threads in both the same and different blocks. This paper focuses on both acceleration and quality of solutions. Computational experiments conducted on many Quadratic Assignment Problem Library (QAPLIB) instances. The experimental results show that PMSA runs up to 29x faster than a single-core CPU and acquires best known solution in a short time in many benchmark datasets.

Identify and Inspect Libraries in Android Applications

Libraries may become a liability for users security. Existing studies show that libraries can be exploited to propagate malware. Hackers utilize fake or modified libraries to execute malicious behaviours. Vetting library instances in applications are desirable. However, it is impeded by the absence of robust library detection method and library vetting method. This paper proposes a hybrid library detection method that it combines name-based method and feature-based method to identify library instances in applications. It can resist simple identifier renaming. Furthermore, this paper proposes an abnormal library detection method that it utilizes frequent pattern to measure the normal degree of library instances. Comparing with existing methods, the abnormal library detection method can not rely on original library files. A ground truth dataset that it consists of 177 malicious applications with abnormal library instance and 81,317 benign apps is used to demonstrate the effectiveness of proposed approaches. Experimental results show that the approaches can precisely detect library instances and effectively reduce the cost of abnormal library detection.

A two-phase local search algorithm for the ordered clustered travelling salesman problem

The ordered clustered travelling salesman problem (OCTSP) is an extension of the classical travelling salesman problem where the set of vertices is partitioned into clusters with a prespecified order. The objective is to find a minimum cost Hamiltonian tour such that the vertices in any cluster are visited contiguously and the clusters are visited in the given order. This paper proposes a two-phase approach using the 2-Opt heuristic and the guided local search (GLS) metaheuristic for obtaining approximate solutions of high-quality to the OCTSP. The first phase attempts to find an optimal Hamiltonian cycle for each cluster. The resulted cycles will be concatenated in the second phase, which then focuses on finding an optimal Hamiltonian cycle for the OCTSP. Computational results confirm the effectiveness of the proposed approach in terms of solution quality and computational time, in comparison with a hybrid genetic algorithm (HGA) on symmetric instances obtained from the travelling salesman problem instance library (TSPLIB).

A two-phase local search algorithm for the ordered clustered travelling salesman problem

The ordered clustered travelling salesman problem (OCTSP) is an extension of the classical travelling salesman problem where the set of vertices is partitioned into clusters with a prespecified order. The objective is to find a minimum cost Hamiltonian tour such that the vertices in any cluster are visited contiguously and the clusters are visited in the given order. This paper proposes a two-phase approach using the 2-Opt heuristic and the guided local search (GLS) metaheuristic for obtaining approximate solutions of high-quality to the OCTSP. The first phase attempts to find an optimal Hamiltonian cycle for each cluster. The resulted cycles will be concatenated in the second phase, which then focuses on finding an optimal Hamiltonian cycle for the OCTSP. Computational results confirm the effectiveness of the proposed approach in terms of solution quality and computational time, in comparison with a hybrid genetic algorithm (HGA) on symmetric instances obtained from the travelling salesman problem instance library (TSPLIB).

GasLib—A Library of Gas Network Instances

The development of mathematical simulation and optimization models and algorithms for solving gas transport problems is an active field of research. In order to test and compare these models and algorithms, gas network instances together with demand data are needed. The goal of GasLib is to provide a set of publicly available gas network instances that can be used by researchers in the field of gas transport. The advantages are that researchers save time by using these instances and that different models and algorithms can be compared on the same specified test sets. The library instances are encoded in an XML (extensible markup language) format. In this paper, we explain this format and present the instances that are available in the library.

Optimizing wind farm cable routing considering power losses

Wind energy is the fastest growing source of renewable energy, but as wind farms are getting larger and more remotely located, installation and infrastructure costs are rising. It is estimated that the expenses for electrical infrastructures account for 15–30% of the overall initial costs, hence it is important to optimize their design. This paper focuses on offshore inter-array cable routing optimization. The routing should connect all turbines to one (or more) offshore substation(s) while respecting cable capacities, no-cross restrictions, connection-limits at the substation, and obstacles at the site. The objective is to minimize both the capital that must be spent immediately in cable and installation costs, and the future reduced revenues due to power losses. We present a Mixed-Integer Linear Programming approach to optimize the routing using both exact and math-heuristic methods. In the power losses computation, wind scenarios are handled efficiently as part of the preprocessing, resulting in a model of only slightly larger size. A library of real-life instances is introduced and made publicly available for benchmarking. Computational results on this testbed show the viability of our methods, proving that savings in the order of millions of Euro can be achieved.

The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

Fuzzy Logic Controller (FLC) has become one of the most frequently utilised algorithms to adapt the metaheuristics parameters as an artificial intelligence technique. In this paper, the 𝜉 parameter of Ant Colony System (ACS) algorithm is adapted by the use of FLC, and its behaviour is studied during this adaptation. The proposed approach is compared with the standard ACS algorithm. Computational results are done based on a library of sample instances for the Traveling Salesman Problem (TSPLIB).

An integration of mixed VND and VNS: the case of the multivehicle covering tour problem

AbstractThe multivehicle covering tour problem (m‐CTP) is a transportation problem with different kinds of locations, where a set of locations must be visited while another set must be close enough to planned routes. Given two sets of vertices V and W, where V represents the set of vertices that may be visited and W is a set of vertices that must be covered by up to m vehicles, the m‐CTP problem is to minimize vehicle routes on a subset of V including T, which represents the subset of vertices that must be visited through the use of potential locations in V. The variant of m‐CTP without a route‐length constraint is treated in this paper. To tackle this problem, we propose a variable neighborhood search heuristic based on variable neighborhood descent method. Experiments were conducted using the datasets based on traveling salesman problem library instances.

Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing – tabu search algorithm to solve the symmetrical traveling salesman problem

This paper applies a hybrid simulated annealing – tabu search algorithm to solve the Traveling Salesman Problem (TSP). Fully considering the characteristics of the hybrid algorithm, we develop a dynamic neighborhood structure for the hybrid algorithm to improve search efficiency by reducing the randomness of the conventional 2-opt neighborhood. A circle-directed mutation is developed to achieve this dynamic neighborhood structure. Furthermore, we propose adaptive parameters that can be automatically adjusted by the algorithm based on context specific examples. This negates the need to frequently readjust algorithm parameters. We employ benchmarks obtained from TSPLIB (a library of sample instances for the TSP) to test our algorithm, and find that the proposed algorithm can obtain satisfactory solutions within a reasonable amount of time. The experimental results demonstrate that the proposed hybrid algorithm can overcome the disadvantages of traditional simulated annealing and tabu search methods. The results also show that the dynamic neighborhood structure is more efficient and accurate than the classical 2-opt. Also, adaptive parameters are appropriate for almost all of the numerical examples tested in this paper. Finally, the experimental results are compared with those of other algorithms, to demonstrate the improved accuracy and efficiency of the proposed algorithm.

Scatter search for the bandpass problem

We tackle a combinatorial problem that consists of finding the optimal configuration of a binary matrix. The configuration is determined by the ordering of the rows in the matrix and the objective function value is associated with a value B, the so-called bandpass number. In the basic version of the problem, the objective is to maximize the number of non-overlapping blocks containing B consecutive cells with a value of one in each column of the matrix. We explore variants of this basic problem and use them to test heuristic strategies within the scatter search framework. An existing library of problem instances is used to perform scientific testing of the proposed search procedures to gain insights that may be valuable in other combinational optimization settings. We also conduct competitive testing to compare outcomes with methods published in the literature and to improve upon previous results.

A large neighbourhood metaheuristic for the risk-constrained cash-in-transit vehicle routing problem

In this paper, we propose a new metaheuristic to solve the Risk constrained Cash-in-Transit Vehicle Routing Problem (Rctvrp). The Rctvrp is a variant of the well-known capacitated vehicle routing problem and models the problem of routing vehicles in the cash-in-transit sector. In the Rctvrp, the risk associated with a robbery represents a critical aspect that is treated as a limiting factor subject to a maximum risk threshold.A new metaheuristic, called aco-lns is developed. It combines the ant colony heuristic for the travelling salesman problem and a variable neighbourhood descent within an large neighbourhood search framework.A new library of Rctvrp instances with known optimal solutions is proposed. The aco-lns is extensively tested on small, medium and large benchmark instances and compared with all existing solution approaches for the Rctvrp.

Improving the Hopfield model performance when applied to the traveling salesman problem

The continuous Hopfield network (CHN) can be used to solve, among other combinatorial optimization problems, the traveling salesman problem (TSP). In order to improve the performance of this heuristic technique, a divide-and-conquer strategy based on two phases is proposed. The first phase involves linking cities with the most neighbors to define a set of chains of cities and, secondly, to join these with isolated cities to define the final tour. Both problems are solved by mapping the two TSPs onto their respective CHNs. The associated parameter-setting procedures are deduced; these procedures ensure the feasibility of the obtained tours, and the quality of the solutions is compared with the pure CHN approach using some traveling salesman problem library (TSPLIB) instances. By means of this strategy, solving TSP instances with sizes of up to 13,509 cities are allowed with the computational resources we had available. Finally, the new divide-and-conquer procedure is improved by tuning the parameter which controls the first phase.

Solving the equality generalized traveling salesman problem using the Lin–Kernighan–Helsgaun Algorithm

The equality generalized traveling salesman problem (E-GTSP) is an extension of the traveling salesman problem (TSP) where the set of cities is partitioned into clusters, and the salesman has to visit every cluster exactly once. It is well known that any instance of E-GTSP can be transformed into a standard asymmetric instance of the TSP, and therefore solved with a TSP solver. This paper evaluates the performance of the state-of-the art TSP solver Lin–Kernighan–Helsgaun (LKH) on transformed E-GTSP instances. Although LKH is used without any modifications, the computational evaluation shows that all instances in a well-known library of benchmark instances, GTSPLIB, could be solved to optimality in a reasonable time. In addition, it was possible to solve a series of new very-large-scale instances with up to 17,180 clusters and 85,900 vertices. Optima for these instances are not known but it is conjectured that LKH has been able to find solutions of a very high quality. The program’s performance has also been evaluated on a large number of instances generated by transforming arc routing problem instances into E-GTSP instances. The program is free of charge for academic and non-commercial use and can be downloaded in source code.

DEFINING TABU TENURE FOR THE QUADRATIC ASSIGNMENT PROBLEM

Tabu search (TS) algorithms are among the most efficient heuristic techniques in combinatorial optimization. Within these algorithms, it is important that the proper policies for maintaining the tabu tenure (tabu list size) are applied. In this paper, we discuss the mechanisms of defining the tabu tenure for the famous combinatorial optimization problem − the quadratic assignment problem (QAP). Several variants of maintaining the tabu tenure are implemented. They are examined on the instances taken from the QAP instances library − QAPLIB. The results from the experiments demonstrate that the deterministic strategies of defining the tabu tenure are preferable to the corresponding random strategies.