General Heuristic Algorithm Research Articles

Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data

Traditional logistics delivery route optimization algorithm has some problems such as long time to find the optimal route. Based on this, this paper discusses swarm intelligence optimization algorithm and logistics delivery route optimization. To solve the logistics vehicle routing problem, considering that the basic ACO (ant colony optimization) has the disadvantages of slow convergence speed and easy to fall into local optimum, this paper proposes a new hybrid population optimization algorithm and applies it to VRPTW (vehicle routing problem with time windows). In addition, the concept of crowding degree in AFSA (artificial fish swarm algorithm) is introduced into ACO. In the early stage of the optimization process, a strong crowding degree limit is set to ensure that most ants are not affected by pheromone concentration to conduct random optimization. The simulation results show that the AC (accuracy rate) of this algorithm is 95.08%, which is higher than the traditional PSO (particle swarm optimization) algorithm and general heuristic algorithm. The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. At the same time, the results fully show that the algorithm in this paper has certain advantages in performance, and it can be applied to logistics delivery route optimization.

Toward Optimally Efficient Search With Deep Learning for Large-Scale MIMO Systems

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at \url{https://github.com/skypitcher/hats}.

3D modelling of a frame assembly using deep learning and the Chu–Liu–Edmonds Algorithm

PurposeThe proposed method is to generate the 3 D model of frame assemblies based on their topological model automatedly. It was a very demanding task and there was no appropriate automated method to facilitate this work.Design/methodology/approachThe proposed method includes two stages. The first stage is decisive. In this stage, a deep learning network and the Chu–Liu–Edmonds algorithm are used to recognize contact relations among parts. Based on this recognition, the authors perform a geometrical computation in the second stage to finalize the 3 D model.FindingsThe authors verify the feasibility of the proposed method using a case study and find that the classification rate of the deep learning network for part contact relations is higher than 75 per cent. Furthermore, more accurate results could be achieved with modification by the Chu–Liu–Edmonds algorithm. The proposed method has lower computational complexity compared with traditional heuristic methods, and its results are more consistent with existing designs.Research limitations/implicationsThe paper introduces machine learning method into assembly modelling issue. The proposed method divides the assembly modelling into two steps and solves the assemble relation creatively.Practical implicationsFrame assemblies are fundamental to many areas. The proposed method could automate frame assembly modelling in a viable way. It could benefit design and manufacture process significantly.Originality/valueThe proposed method expands the application of machine learning into a new field. It would be more useful than simple machine learning in industry. The proposed method is better than general heuristic algorithms. It outputs identical results when the inputs are the same. Meanwhile, the algorithmic complexity in worst situation is better than general heuristic algorithms.

A scheduling algorithm based on the singular value decomposition heuristic method in a distributed manufacturing system

AbstractBecause distributed manufacturing technology is the foundation of modernized production and traditional heuristic methods exhibit problems of high complexity and low efficiency, this paper designs a scheduling algorithm based on the singular value decomposition heuristic (SVDH) method. The algorithm uses the device distribution and the transportation relationship between devices in a distributed manufacturing system. The algorithm takes the sequence relationship between tasks and the distance between devices as the implicit relationship between the task and the device. The algorithm makes use of the implicit relationship to amend the processing time matrix of the task and corrects the processing time matrix that contains the transportation relationship. Singular value decomposition principal component analysis is performed on the corrected processing time to find the most suitable processing device for each process, and an initial solution matrix is established. The heuristic solution is used to optimize the initial solution to find the optimal scheduling result based on the initial solution matrix. The establishment of the initial solution can effectively reduce the computational complexity of the heuristic solution, realize a parallelizing solution, and improve the efficiency of the heuristic solutions. In addition, the SVDH scheduling result has a lower transfer time between devices due to the consideration of the topology of tasks and devices, that is, the transit time. In this paper, the experiments are conducted on the heuristic performance, scheduling results, and transportation time. The experimental results show the advantages of SVDH over general heuristic algorithms in terms of efficiency and transit time.

Heuristic algorithm based on reduce and optimize approach for a selective and periodic inventory routing problem in a waste vegetable oil collection environment

This paper develops a general heuristic algorithm to solve the selective and periodic inventory routing problem (SPIRP) in a waste vegetable oil collection environment. In the past, the SPIRP has been formulated and solved via a valid set of inequalities and an adaptive large-neighborhood search algorithm. The proposed algorithm is based on a reduce and optimize approach (ROA) and a new inequality. The ROA always solves the problem using a small feasible subset containing a near-optimal solution. Two available sets of benchmark instances are tested and solved: (a) in the first set with 36 instances, the new algorithm improves the reported solution in 94.44% of the instances; (b) in the second set with 54 instances, the results show that the proposed algorithm finds a better solution than the previously published ones in 92.59% of the cases. In a third set composed of 24 very large instances, the proposed heuristic algorithm always finds better solutions than the CPLEX MIP solver. Finally, the computational results show that the proposed algorithm obtains, on average, a solution within 1.99%, 2.86%, and 7.41% of optimality for the first, second, and third set of instances, respectively. Also, the computational experiments show that the heuristic algorithm is effective and efficient in instances with up to 300 source nodes.

Radio Channel Allocations With Global Optimality and Bounded Computational Scale

The radio channel assignment (RCA) in wireless networks is an optimization problem that is often found NP-complete. For networks of practical sizes, various heuristic algorithms are used to solve it. However, there are two major issues: finding a globally optimized solution without relying on specific interference models and estimating the computational complexity of general heuristic algorithms. In this paper, we propose a new simulated annealing (SA)-based RCA (SRCA) algorithm to find the globally optimized channel assignment in a distributed way but with bounded computational complexity. We propose using effective channel utilization (ECU) as the evaluation vector, whereas the objective function is to maximize the total ECU in a neighborhood. The ECU can be easily calculated by an access point (AP). The impact of interference is included in the ECU. We propose a hybrid method for estimating the algorithm's computational scale (CS), i.e., the number of channel reallocations until the network reaches a convergence state, by combining analytical and experimental methods. The resulting algorithm is a dynamic and distributed algorithm. Our extensive simulation results have demonstrated that it quickly achieves 99% of the global maximum with a chance over 95%, whereas its complexity is linear with the number of routers in the network.

Learning from the Metaheuristics: Protocols for Automated Negotiations

Nowadays, enterprises are more and more interconnected such that operational planning has to consider the different interests of the involved organizations. This may be a challenging and complex task as it is subject to strategic interactions and incomplete information. Automated negotiation by software agents is a powerful tool which can handle these issues and facilitate intercompany planning. Nevertheless, sophisticated negotiation protocols that govern the rules of the negotiation are needed. In this study, we present and evaluate two configurable protocols for multi-issue negotiations, which are inspired by general heuristic optimization algorithms for centralized problems, so-called metaheuristics. The protocols consist of several policy building blocks; these are evaluated with regard to their impact on the negotiation outcome. The evaluation shows that both protocols can efficiently achieve beneficial solutions—even for complex, nonlinear contract spaces—given the parameterization and the configuration of building blocks are chosen appropriately. Furthermore, we elaborate on requirements for appropriate protocol design and find that both protocols adequately comply with the requirements.

Study on the Algorithm for Train Operation Adjustment Based on Ordinal Optimization

It is a crucial and difficult problem in railway transportation dispatch mechanism to automatically compile train operation adjustment (TOA) plan with computer to ensure safe, fast, and punctual running of trains. Based on the proposed model of TOA under the conditions of railway network (RN), we take minimum travel time of train as objective function of optimization, and after fast preliminary evaluation calculation on it, we introduce the theory and method of ordinal optimization (OO) to solve it. This paper discusses in detail the implementation steps of OO algorithm. A practical calculation example of Datong-Qinhuangdao (hereinafter referred to as Da-Qin) heavy haul railway is performed with the proposed algorithm to prove that OO can ensure getting good enough solution with high probability. Particularly, for complex optimization problems with large amount of calculation, OO can greatly increase computational efficiency, and it can save at least one order of magnitude of calculation amount than general heuristic algorithm. Thus, the proposed algorithm can well satisfy the requirements in engineering.

Strategic Design of Distribution System of State-owned Companies : Solution Development

A method is presented to solve location problem of PSO-SOC, which is characterized as Minimum Concave-cost Multi-commodity Flow (MCMF) problem. Core idea of proposed solution procedure is the exploitation of network representation (NR). It is found that despite the solution is approached by an aggregate flow, our network representation could derive disaggregate flows, especially for those which deal with diverse cost functions among each commodity, such as production cost and revenue. In addition, our NR could deal with the situation where total supply is not in balance with total demand. The other characteristic of proposed solution procedure is that location decision of MCMF-NR is not represented by the binary number, as employed on most of location model. Location decision is represented by the flow on links associated to fixed-cost of facility. General heuristic algorithm related to Destination Spanning Tree development is suggested to solve the MCMF-NR, and an illustrative example is also discussed.

One in a million: picking the right patterns

Constrained pattern mining extracts patterns based on their individual merit. Usually this results in far more patterns than a human expert or a machine leaning technique could make use of. Often different patterns or combinations of patterns cover a similar subset of the examples, thus being redundant and not carrying any new information. To remove the redundant information contained in such pattern sets, we propose two general heuristic algorithms—Bouncer and Picker—for selecting a small subset of patterns. We identify several selection techniques for use in this general algorithm and evaluate those on several data sets. The results show that both techniques succeed in severely reducing the number of patterns, while at the same time apparently retaining much of the original information. Additionally, the experiments show that reducing the pattern set indeed improves the quality of classification results. Both results show that the developed solutions are very well suited for the goals we aim at.

A Set-Covering-Based Heuristic Approach for Bin-Packing Problems

Several combinatorial optimization problems can be formulated as large set-covering problems. In this work, we use the set-covering formulation to obtain a general heuristic algorithm for this type of problem, and describe our implementation of the algorithm for solving two variants of the well-known (one-dimensional) bin-packing problem: the two-constraint bin-packing problem and the basic version of the two-dimensional bin-packing problem, where the objects cannot be rotated and no additional requirements are imposed. In our approach, both the “column-generation” and the “column-optimization” phases are heuristically performed. In particular, in the first phase, we do not generate the entire set of columns, but only a small subset of it, by using greedy procedures and fast constructive heuristic algorithms from the literature. In the second phase, we solve the associated set-covering instance by means of a Lagrangian-based heuristic algorithm. Extensive computational results on test instances from the literature show that, for the two considered problems, this approach is competitive, with respect to both the quality of the solution and the computing time, with the best heuristic and metaheuristic algorithms proposed so far.

Evaluation of algorithms for efficient partitioning and scheduling of data bursts in satellite communications

We evaluate four scheduling algorithms for satellite communications that use the Time Division Multiple Access methodology. All the algorithms considered are based on the open-shop model. The open-shop model is suitably represented or modified to exploit some existing algorithms to solve the satellite communication problem. In the first two algorithms, namely pre-emptive scheduling with no intersatellite links and greedy heuristics with two intersatellite links, a (traffic) matrix representation of the open-shop model is used to get a near optimal schedule. In the next two algorithms, generalized heuristic algorithm and the branch and bound algorithm, the open-shop model is modified to accommodate the inter-satellite link and this modified open-shop model is used to solve for a near optimal schedule. The basic methodology of all the algorithms are briefly described and their performance was evaluated through extensive simulations. The performance criteria to evaluate the algorithms are—run time of the algorithms, schedule lengths, and optimality of the algorithm against theoretical bounds. Three of the above-mentioned algorithms are evaluated by comparing the performance criteria under similar conditions. Optimal branch and bound algorithm is not evaluated due to its high complexity. The general heuristic algorithm is found to give a good trade off between computation time and optimality. The computation time is comparable with the pre-emptive scheduling algorithm and greedy heuristic algorithm and the schedule length achieved is near to the lower bound value. Copyright © 2005 John Wiley & Sons, Ltd.

Completeness and Admissibility for General Heuristic Search Algorithms—A Theoretical Study: Basic Concepts and Proofs

We propose a formal generalization for various works dealing with Heuristic Search in State Graphs. This generalization focuses on the properties of the evaluation functions, on the characteristics of the state graphs, on the notion of path length, on the procedures that control the node expansions, on the rules that govern the update operations. Consequently, we present the algorithm family r and the sub-family A, which include Nilsson‘s A or A^a and many of their successors such as HPA, B, A^a_e, A_e, C, BF^a, B′, IDA^a, D, A^aa, SDW. We prove general theorems about the completeness and the sub-admissibility that widely extend the previous results and provide a theoretical support for using diverse kinds of Heuristic Search algorithms in enlarged contexts, specially when the state graphs and the evaluation functions are less constrained than ordinarily.

A comparison of two methods for solving 0–1 integer programs using a general purpose simulated annealing algorithm

0–1 problems are often difficult to solve. Although special purpose algorithms (exact as well as heuristic) exist for solving particular problem classes or problem instances, there are few general purpose algorithms for solving practical-sized instances of 0–1 problems. This paper deals with a general purpose heuristic algorithm for 0–1 problems. In this paper, we compare two methods based on simulated annealing for solving general 0–1 integer programming problems. The two methods differe in the scheme used for neighbourhood transitions in the simulated annealing framework. We compare the performance of the two methods on the set partitioning problem.

On the conditional p-median problem

In this paper we investigate the conditional p-median problem. Optimal algorithms for the Euclidean case in the plane for the 1-median with several existing facilities are proposed. A general heuristic algorithm for any metric or environment (network or continuous space) is presented. The algorithm is based on solving several p-median problems.

Linear-space best-first search

Best-first search is a general heuristic search algorithm that always expands next a frontier node of lowest cost. It includes as special cases breadth-first search, Dijkstra's single-source shortest-path algorithm, and the A∗ algorithm. Its applicability, however, is limited by its exponential memory requirement. Previous approaches to this problem, such as iterative deepening, do not expand nodes in best-first order if the cost function can decrease along a path. We present a linear-space best-first search algorithm (RBFS) that always explores new nodes in best-first order, regardless of the cost function, and expands fewer nodes than iterative deepening with a nondecreasing cost function. On the sliding-tile puzzles, RBFS with a nonmonotonic weighted evaluation function dramatically reduces computation time with only a small penalty in solution cost. In general, RBFS reduces the space complexity of best-first search from exponential to linear, at the cost of only a constant factor in time complexity in our experiments.