• Title/Summary/Keyword: Heuristics for $A^*$ algorithm

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Model and Heuristics for the Heterogeneous Fixed Fleet Vehicle Routing Problem with Pick-Up and Delivery

  • Zhai, Shuai;Mao, Chao
    • Journal of Distribution Science
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    • v.10 no.12
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    • pp.19-24
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    • 2012
  • Purpose - This paper discusses the heterogeneous fixed fleet vehicle routing problem with pick-up and delivery (HFFVRPPD), for vehicles with different capacities, fixed costs, and travel costs. Research Design, data, methodology - This paper made nine assumptions for establishing a mathematical model to describe HFFVRPPD. It established a practical mathematical model, and because of the non-deterministic polynomial-time hard (NP-hard), improved the traditional simulated annealing algorithm and tested a new algorithm using a certain scale model. Result - We calculated the minimum cost of the heterogeneous fixed fleet vehicle routing problem (HFFVRP) with a single task and, on comparing the results with the actual HFFVRP for the single task alone, observed that the total cost of HFFVRPPD reduced significantly by 46.7%. The results showed that the new algorithm provides better solutions and stability. Conclusions - This paper, by comparing the HFFVRP and HFFVRPPD results, highlights certain advantages of using HFFVRPPD in physical distribution enterprises, such as saving distribution vehicles, reducing logistics cost, and raising economic benefits.

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Meta-Heuristic Algorithm Comparison for Droplet Impingements (액적 충돌 현상기반 최적알고리즘의 비교)

  • Joo Hyun Moon
    • Journal of ILASS-Korea
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    • v.28 no.4
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    • pp.161-168
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    • 2023
  • Droplet impingement on solid surfaces is pivotal for a range of spray and heat transfer processes. This study aims to optimize the cooling performance of single droplet impingement on heated textured surfaces. We focused on maximizing the cooling effectiveness or the total contact area at the droplet maximum spread. For efficient estimation of the optimal values of the unknown variables, we introduced an enhanced Genetic Algorithm (GA) and Particle swarm optimization algorithm (PSO). These novel algorithms incorporate its developed theoretical backgrounds to compare proper optimized results. The comparison, considering the peak values of objective functions, computation durations, and the count of penalty particles, confirmed that PSO method offers swifter and more efficient searches, compared to GA algorithm, contributing finding the effective way for the spray and droplet impingement process.

Robust Quick String Matching Algorithm for Network Security (네트워크 보안을 위한 강력한 문자열 매칭 알고리즘)

  • Lee, Jong Woock;Park, Chan Kil
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.9 no.4
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    • pp.135-141
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    • 2013
  • String matching is one of the key algorithms in network security and many areas could be benefit from a faster string matching algorithm. Based on the most efficient string matching algorithm in sual applications, the Boyer-Moore (BM) algorithm, a novel algorithm called RQS is proposed. RQS utilizes an improved bad character heuristic to achieve bigger shift value area and an enhanced good suffix heuristic to dramatically improve the worst case performance. The two heuristics combined with a novel determinant condition to switch between them enable RQS achieve a higher performance than BM both under normal and worst case situation. The experimental results reveal that RQS appears efficient than BM many times in worst case, and the longer the pattern, the bigger the performance improvement. The performance of RQS is 7.57~36.34% higher than BM in English text searching, 16.26~26.18% higher than BM in uniformly random text searching, and 9.77% higher than BM in the real world Snort pattern set searching.

A Boolean Equivalence Testing Algorithm based on a Derivational Method

  • Moon, Gyo-Sik
    • Journal of Electrical Engineering and information Science
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    • v.2 no.5
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    • pp.1-8
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    • 1997
  • The main purpose of the Boolean equivalence problem is to verify that two Boolean expressions have the same functionality. Simulation has been extensively used as the standard method for the equivalence problem. Obviously, the number of tests required to perform a satisfactory coverage grows exponentially with the number of input variables. However, formal methods as opposed to simulation are getting more attention from the community. We propose a new algorithm called the Cover-Merge Algorithm based on a derivational method using the concept of cover and merge for the equivalence problem and investigate its theoretical aspects. Because of the difficulty of the problem, we emphasize simplification techniques in order to reduce the search space or problem size. Heuristics based on types of merges are developed to speed up the derivation process by allowing simplifications. In comparison with widely used technique called Binary Decision Diagram or BDD, the algorithm proposed outperforms BDD in nearly all cases of input including standard benchmark problems.

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An algorithm for pattern recognition of multichannel ECG signals using AI (AI기법을 이용한 멀티채널 심전도신호의 패턴인식 알고리즘)

  • 신건수;이병채;황선철;이명호
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10a
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    • pp.575-579
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    • 1990
  • This paper describes an algorithm that can efficiently analyze the multichannel ECG signal using the frame. The input is a set of significant features (points) which have been extracted from an original sampled signal by using the split-and-merge algorithm. A signal from each channel can be hierarchical ADN/OR graph on the basis of the priori knowledge for ECG signal. The search mechanisms with some heuristics and the mixed paradigms of data-driven hypothesis formation are used as the major control mechanisms. The mutual relations among features are also considered by evaluating a score based on the relational spectrum. For recognition of morphologies corresponding to OR nodes, an hypothesis modification strategy is used. Other techniques such as instance, priority update of prototypes, and template matching facility are also used. This algorithm exactly recognized the primary points and supporting points from the multichannel ECG signals.

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Capacitated Fab Scheduling Approximation using Average Reward TD(${\lambda}$) Learning based on System Feature Functions (시스템 특성함수 기반 평균보상 TD(${\lambda}$) 학습을 통한 유한용량 Fab 스케줄링 근사화)

  • Choi, Jin-Young
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.34 no.4
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    • pp.189-196
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    • 2011
  • In this paper, we propose a logical control-based actor-critic algorithm as an efficient approach for the approximation of the capacitated fab scheduling problem. We apply the average reward temporal-difference learning method for estimating the relative value functions of system states, while avoiding deadlock situation by Banker's algorithm. We consider the Intel mini-fab re-entrant line for the evaluation of the suggested algorithm and perform a numerical experiment by generating some sample system configurations randomly. We show that the suggested method has a prominent performance compared to other well-known heuristics.

Improved Algorithms for Minimum Cost Replicated Web Contents Distribution Tree (통신비용을 최소화하는 복제 웹컨텐츠 분배나무 구성을 위한 개선된 알고리즘)

  • Hong Sung-Pil;Lee Dong-Gwon
    • Korean Management Science Review
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    • v.22 no.2
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    • pp.99-107
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    • 2005
  • Recently, Tang and Chanson proposed a minimum cost distribution model for replicated Web contents subject to an expiration-based consistency management. Their model is a progress in that it can consider multiple replicas via the network of servers located on the Web. The proposed greedy heuristic, however, has an undesirable feature that the solution tends to converge a local optimum at an early stage of the algorithm. in this paper, we propose an algorithm based on a simple idea of preventing the early local convergence. The new algorithm provides solutions whose cost are, on the average, 27$\%$ lower than in the previous algorithm.

Efficient Elitist Genetic Algorithm for Resource-Constrained Project Scheduling

  • Kim, Jin-Lee
    • Korean Journal of Construction Engineering and Management
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    • v.8 no.6
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    • pp.235-245
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    • 2007
  • This research study presents the development and application of an Elitist Genetic Algorithm (Elitist GA) for solving the resource-constrained project scheduling problem, which is one of the most challenging problems in construction engineering. Main features of the developed algorithm are that the elitist roulette selection operator is developed to preserve the best individual solution for the next generation so as to obtain the improved solution, and that parallel schedule generation scheme is used to generate a feasible solution to the problem. The experimental results on standard problem sets indicate that the proposed algorithm not only produces reasonably good solutions to the problems over the heuristic method and other GA, but also can find the optimal and/or near optimal solutions for the large-sized problems with multiple resources within a reasonable amount of time that will be applicable to the construction industry. This paper will help researchers and/or practitioners in the construction project scheduling software area with alternative means to find the optimal schedules by utilizing the advantages of the Elitist GA.

Integrated Vehicle Routing Model for Multi-Supply Centers Based on Genetic Algorithm (유전자알고리즘 및 발견적 방법을 이용한 차량운송경로계획 모델)

  • 황흥석
    • Journal of the Korea Society for Simulation
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    • v.9 no.3
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    • pp.91-102
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    • 2000
  • The distribution routing problem is one of the important problems in distribution and supply center management. This research is concerned with an integrated distribution routing problem for multi-supply centers based on improved genetic algorithm and GUI-type programming. In this research, we used a three-step approach; in step 1 a sector clustering model is developed to transfer the multi-supply center problem to single supply center problems which are more easy to be solved, in step 2 we developed a vehicle routing model with time and vehicle capacity constraints and in step 3, we developed a GA-TSP model which can improve the vehicle routing schedules by simulation. For the computational purpose, we developed a GUI-type computer program according to the proposed methods and the sample outputs show that the proposed method is very effective on a set of standard test problems, and it could be potentially useful in solving the distribution routing problems in multi-supply center problem.

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WWW-based Vehicle Routing Problem using Mixed Genetic Algorithm (혼합형 유전자 알고리즘을 이용한 웹 기반의 차량 경로 문제)

  • 김기섭;양병학
    • Journal of the military operations research society of Korea
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    • v.24 no.2
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    • pp.117-129
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    • 1998
  • This study is concerned with developing a heuristic for a web-based vehicle routing problem using mixed genetic algorithm(VRPMGA) which determines each vehicle route in order to minimize the transportation costs, subject to meeting the demands of all delivery points. VRP is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study aims to develop a mixed genetic algorithm by partitioned strategy which can give a good solution in comparatively brief time. The good features of the VRPMGA are, fristly, the ability of early convergence and, secondly, the capability of producing multiple, alternative, and near-optimal solutions. The VRPMGA is a useful algorithm that can be appliable to VRP and TSP. Finally, the computational test were performed using the benchmark problems and the proposed heuristic is compared with the other existing algorithms (COSA). The result of computational tests shows that proposed heuristic gives good solutions, in much shorter time, which are same as the best known solutions in the pervious research.

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