Algorithmic aspects for functional partitioning and scheduling in hardware/software co-design

Algorithmic aspects for multiple-choice hardware/software partitioning

When the project is scheduled with a given set of resources, it is difficult to find the optimal solution. Resourceconstrained scheduling problems (RCPSP) are generally NP-hard. In this paper, a high level heuristic procedure “Tabu Search Algorithm (TSA)” is proposed to provide good solutions to resource-constrained, deterministic activity duration project scheduling problems. We present the application results of the computational tabu search and OPL-CPLEX algorithm and compare them with that of earlier applicable researches along with a discussion about further research. Our computational results are presented, which establish the superiority of tabu search over the existing heuristic algorithms. Two different solution strategies are also discussed, namely tabu search and OPLCPLEX exact algorithm approach which can be used with the proposed model. Due to the execution time, we have shown that OPL-CPLEX's algorithm is a valid method with medium scale RCPSPs. For the considered deterministic problems, a good agreement has been obtained between theoretical and experimental results.

The shortest route finding problem is a significant topic in graph theory and combinatorial optimization, with wide applications in logistics, transportation, and scheduling. This research aims to improve the quality of the solution and time efficiency in solving the Traveling Salesman Problem (TSP) by optimizing the Cheapest Insertion Heuristic (CIH) algorithm using the application of the Tabu Search algorithm. The CIH algorithm constructs an initial solution by inserting points based on minimum weight. At the same time, the Tabu Search algorithm is applied to enhance the solution by avoiding local optima using a tabu list mechanism. The research data, consisting of the distances between parking retribution collection points by the Malang City Transportation Agency in Sukun Sub-district, were obtained from Google Maps. The algorithm performance evaluation is done by comparing the total mileage before and after optimization, and statistically analyzed using the Wilcoxon signed-rank test because the data does not follow a normal distribution. The results showed that optimizing the CIH algorithm using the Tabu Search algorithm significantly resulted in routes with shorter travel distances than using the CIH algorithm alone. This finding proves that optimizing the CIH algorithm with Tabu Search increases the effectiveness of finding the shortest route.The shortest route finding problem is a significant topic in graph theory and combinato-rial optimization, with wide applications in logistics, transportation, and scheduling. Thisresearch aims to improve the quality of the solution and time efficiency in solving the Trav-eling Salesman Problem (TSP) by optimizing the Cheapest Insertion Heuristic (CIH) algo-rithm using the application of the Tabu Search algorithm. The CIH algorithm constructs aninitial solution by inserting points based on minimum weight. At the same time, the TabuSearch algorithm is applied to enhance the solution by avoiding local optima using a tabulist mechanism. The research data, consisting of the distances between parking retributioncollection points by the Malang City Transportation Agency in Sukun Sub-district, were ob-tained from Google Maps. The algorithm performance evaluation is done by comparing thetotal mileage before and after optimization, and statistically analyzed using the Wilcoxonsigned-rank test because the data does not follow a normal distribution. The results showedthat optimizing the CIH algorithm using the Tabu Search algorithm significantly resulted inroutes with shorter travel distances than using the CIH algorithm alone. This finding provesthat optimizing the CIH algorithm with Tabu Search increases the effectiveness of findingthe shortest route.

In current research, a parallel-machine scheduling problem with a material handling device in PCB production environment is investigated. The purpose of the current research is to find an optimal processing sequence for PCB production in electroplating process. First the hoist movement rule in a real electroplating line is analyzed and a mixed integer programming model is formulated to describe the current problem. Later a heuristic called S is proposed to obtain the initial feasible solution. Moreover, tabu search (TS) algorithm is presented to optimize the obtained initial solution from the proposed S heuristic algorithm. Experiments are performed on the real case problems and the performance of proposed TS is compared with standard genetic algorithm (GA). Results indicate that the proposed TS algorithm performs much better as compared to GA both in effectiveness and convergence.

PD. Kebersihan Kota Bandung has been facing one of the problems related to vehicle routing since the route used for waste transport by the company currently does not pay attention to the location and distance of the TPS to be visited, resulting in a longer total distance of 564, 30 km. With the current routes, the company does not have a definite schedule of waste transport, this problem concern involve for waste heap. Therefore, VRP research was conducted to determine the transportation of waste routes in West Bandung area by producing solutions that can be proposed to reduce the total distances. The research was solved using the Tabu Search method, the application of this method requires the initial solution. In this study, the saving and sequential method insertion used to create the initial solution, then the initial solution was done repair by using the Tabu Search algorithm. The result of data processing with tabu Search generates 15 routes with the total mileage for each day of 448.48 km. Total distance generated by Tabu Search resulted in a decline of 115.82 km or give a savings of 20.53% from total distance with the current route.

Embedded systems have a wide range as they have become essential parts of modern life. A typical embedded system consists of application-specific hardware and programmable software. Hardware-software (HW/SW) partitioning problem defines which tasks should be mapped to software and hardware. It allows the proposition of an optimised system with predefined constraints. In this paper, a heuristic algorithm, the hybrid-bee-colony-optimisation for multiple-choice HW/SW partitioning is proposed. The proposed algorithm aims to minimise power consumption and execution time, while meeting area constraint. This heuristic algorithm is developed to generate an approximate solution in acceptable delay. The Virtex 5 is chosen as a target platform. Simulation results are compared with existing works and they show rapidity with the generation of an optimal solution near to the exact one.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

2차원 저장소 적재는 NP-hard 문제로서 그 문제의 정확한 해를 구하는 것이 어려운 것으로 알려져 있으며, 이의 더 좋은 해를 얻기 위해 유전자(genetic) 알고리즘, 시뮬레이티드 어닐링(simulated annealing), 타부서치(tabu search)등과 같은 근사적 접근법이 제안되어 왔다. 하지만 분지한계(branch-and-bound)나 타부서치 기법들을 이용한 기존의 대표적인 근사 알고리즘들은 휴리스틱 알고리즘의 해에 기반을 둠으로 효율성이 낮고 반복수행에 의한 계산시간이 길다. 따라서 본 논문에서는 이러한 근사 알고리즘의 복잡성을 간소화하고, 알고리즘의 효율성을 높이기 위해 적재가능성을 판단하는 적합성 함수(fitness function)를 정의하고 이를 이용하여 어떤 특정 개체의 적재영역을 판단하는데 영향을 주는 적재영역의 수를 계산한다. 또한, 이들을 이용한 새로운 휴리스틱 알고리즘을 제안하였다. 끝으로 기존의 휴리스틱 또는 메타휴리스틱 기법과의 비교실험을 통해 기존의 휴리스틱 알고리즘인 FFF와 FBS에 비해 97%의 결과가 같거나 우수하였으며, 타부서치 알고리즘에 비해 86%의 결과가 같거나 우수한 것으로 나타났다. The two-dimensional bin packing problem(2D-BPP) has been known to be NP-hard, and it is difficult to solve the problem exactly. Many approximation methods, such as genetic algorithm, simulated annealing and tabu search etc, have been also proposed to gain better solutions. However, the existing approximation algorithms, such as branch-and-bound and tabu search, have shown the low efficiency and the long execution time due to a large of iterations. To solve these problems, we first define the fitness function to simplify and increase the utility of algorithm. The function decides whether an item is packed into a given area, and as an important information for a packing strategy, the number of subarea that can accommodate a given item is obtained from the variant of the fitness function. Then we present a heuristic algorithm BF for 2D bin packing, constructed by the fitness function and subarea. Finally, the effectiveness of the proposed algorithm will be expressed by the comparison experiments with the heuristic and the metaheuristic of the literatures. As comparing with existing heuristic algorithms and metaheuristic algorithms, it has been found that the packing rate of algorithm BP is the same as 97% as existing heuristic algorithms, FFF and FBS, or better than them. Also, it has been shown the same as 86% as tabu search algorithm or better.

The container stowage planning problem (CSPP) presented in this paper considers the task of loading containers into the containership with a set of structural and operational restrictions. We formulate this problem as an integer programming model to minimize the number of shift, the total loading time and the weight difference between maximum loaded bay and minimum loaded bay. For solving this problem, we decompose this problem into former subproblem and latter subproblem and solving two subproblems orderly to obtain the initial solutions. Based on this initial solution, the tabu search algorithm with diverse neighborhoods and diversification strategy are proposed. The experimental results comparing with CPLEX show that the proposed mathematical model and the tabu search algorithm can obtain better solutions than CPLEX in an allowable time and resolve the problem more effectively than current plan operator.

Tabu search is one of the most effective heuristics for locating high-quality solutions to a diverse array of NP-hard combinatorial optimization problems. Despite the widespread success of tabu search, researchers have a poor understanding of many key theoretical aspects of this algorithm, including models of the high-level run-time dynamics and identification of those search space features that influence problem difficulty. We consider these questions in the context of the job-shop scheduling problem (JSP), a domain where tabu search algorithms have been shown to be remarkably effective. Previously, we demonstrated that the mean distance between random local optima and the nearest optimal solution is highly correlated with problem difficulty for a well-known tabu search algorithm for the JSP introduced by Taillard. In this paper, we discuss various shortcomings of this measure and develop a new model of problem difficulty that corrects these deficiencies. We show that Taillard's algorithm can be modeled with high fidelity as a simple variant of a straightforward random walk. The random walk model accounts for nearly all of the variability in the cost required to locate both optimal and sub-optimal solutions to random JSPs, and provides an explanation for differences in the difficulty of random versus structured JSPs. Finally, we discuss and empirically substantiate two novel predictions regarding tabu search algorithm behavior. First, the method for constructing the initial solution is highly unlikely to impact the performance of tabu search. Second, tabu tenure should be selected to be as small as possible while simultaneously avoiding search stagnation; values larger than necessary lead to significant degradations in performance.

A developed Tabu Search algorithm for heterogeneous fleet vehicle routing problem

The loss minimization problem is one of the most important problems to save the operational cost in distribution systems. Therefore, more efficient approaches are required to handle such a combinatorial problem. This paper presents an efficient meta-heuristic method for reconfiguration of distribution systems. A Tabu Search (TS) algorithm is used to reconfigure distribution systems so that active power losses are globally minimized with turning on / off sectionalizing switches. TS algorithm is introduced with some modifications such as using a tabu list with variable size according to the system size; this should lead to robust algorithm, and prevent cycling. Also, a random multiplicative move is used in the search process to diversify the search toward unexplored regions. A new method to check the radial topology of the system is presented. The proposed method is applied to 32-node and 69-node distribution systems. The obtained results using the proposed TS approach are compared with results obtained using Simulated Annealing (SA) approach and Parallel Tabu Search (PTS) approach in the previous work to examine the performance.

This paper addresses the way in which heuristic algorithms can be used to solve the n-queen problem. Metaheuristics for algorithm simulated annealing, tabu search and genetic algorithm are shown, test results are demonstrated and upper bound complexity is determined. The efficiencies of algorithms are compared and their achievements are measured. Due to the reduction of the fitness function complexity to O(1) problem instances with large dimensions are solved.

This study presents two enhanced learning algorithms used for discovering probabilistic graphical models based on the Bayesian Network (BN) structure. The two heuristic structure learning algorithms, namely Tabu Search (TS) and Simulated Annealing (SA), were empirically evaluated and compared regarding efficiency. These algorithms were applied to real-life data sets for the vertebral column. A data set containing values for six biomechanical features was used to classify patients into three categories, namely, Disk Hernia (DH), Spondylolisthesis (SL), and Normal (NO), and two categories, namely, Abnormal (AB) and NO. The results indicated that SA is a more effective algorithm than TS. However, the empirical results obtained using TS indicated that the TS algorithm is promising because of its relatively simple network structure.

Submission of information relating to college scheduling in college is very important and is usually done on a semester by the academic section. One of these universities is Universitas Kompueter Indonesia (Unikom) which became object of case study. In determining the scheduling of many aspects that must be considered include the components of the course, lecturer, class, space, and time. All of these components are considered so that the schedule of lectures that will be formed does not happen clashing with each other. In addition, scheduling problems will be more complex if there are many additional terms or constraints. In this study aims to be able to make lecture class scheduling replacement with a combination of genetic algorithms and tabuo search. The first step is done by separating the hard and soft constraints that exist in the lecture problem, and then the best individual search is done by cross-over or cross-breeding process to produce new individual and perform the process of mutation or exchange of individual position in order to produce the best fitness value of the previous individual. The advantage of using a tabu search algorithm allows for the absence of duplicate individual processes. Therefore, the combination of these two algorithms is capable of producing good scheduling and the clashing rate of a schedule can be handled.