A memetic algorithm for the flexible flow line scheduling problem with processor blocking

This paper introduces an efficient memetic algorithm (MA) combined with a novel local search engine, namely, nested variable neighbourhood search (NVNS), to solve the flexible flow line scheduling problem with processor blocking (FFLB) and without intermediate buffers. A flexible flow line consists of several processing stages in series, with or without intermediate buffers, with each stage having one or more identical parallel processors. The line produces a number of different products, and each product must be processed by at most one processor in each stage. To obtain an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Our proposed MA employs a new representation, operators, and local search method to solve the above-mentioned problem. The computational results obtained in experiments demonstrate the efficiency of the proposed MA, which is significantly superior to the classical genetic algorithm (CGA) under the same conditions when the population size is increased in the CGA.     

A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands

The multi-compartment vehicle routing problem (MC-VRP) consists of designing transportation routes to satisfy the demands of a set of customers for several products that, because of incompatibility constraints, must be loaded in independent vehicle compartments. Despite its wide practical applicability the MC-VRP has not received much attention in the literature, and the few existing methods assume perfect knowledge of the customer demands, regardless of their stochastic nature. This paper extends the MC-VRP by introducing uncertainty on what it is known as the MC-VRP with stochastic demands (MC-VRPSD). The MC-VRPSD is modeled as a stochastic program with recourse and solved by means of a memetic algorithm. The proposed memetic algorithm couples genetic operators and local search procedures proven to be effective on deterministic routing problems with a novel individual evaluation and reparation strategy that accounts for the stochastic nature of the problem. The algorithm was tested on instances of up to 484 customers, and its results were compared to those obtained by a savings-based heuristic and a memetic algorithm (MA/SCS) for the MC-VRP that uses a spare capacity strategy to handle demand fluctuations. In addition to effectively solve the MC-VRPSD, the proposed MA/SCS also improved 14 best known solutions in a 40-problem testbed for the MC-VRP.     

Learning concepts in parallel based upon the strategy of versionspace

Applies the technique of parallel processing to concept learning. A parallel version-space learning algorithm based upon the principle of divide-and-conquer is proposed. Its time complexity is analyzed to be O(k log₂n) with n processors, where n is the number of given training instances and k is a coefficient depending on the application domains. For a bounded number of processors in real situations, a modified parallel learning algorithm is then proposed. Experimental results are then performed on a real learning problem, showing that our parallel learning algorithm works, and being quite consistent with the results of theoretical analysis. We conclude that when the number of training instances is large, it is worth learning in parallel because of its faster execution     

An efficient memetic algorithm for the uncapacitated single allocation hub location problem

In this paper, we present a memetic algorithm (MA) for solving the uncapacitated single allocation hub location problem (USAHLP). Two efficient local search heuristics are designed and implemented in the frame of an evolutionary algorithm in order to improve both the location and allocation part of the problem. Computational experiments, conducted on standard CAB/AP hub data sets (Beasley in J Global Optim 8:429–433, 1996) and modified AP data set with reduced fixed costs (Silva and Cunha in Computer Oper Res 36:3152–3165, 2009), show that the MA approach is superior over existing heuristic approaches for the USAHLP. For several large-scale AP instances up to 200 nodes, the MA improved the best-known solutions from the literature until now. Numerical results on instances with 300 and 400 nodes introduced in Silva and Cunha (Computer Oper Res 36:3152–3165, 2009) show significant improvements in the sense of both solution quality and CPU time. The robustness of the MA was additionally tested on a challenging set of newly generated large-scale instances with 520–900 nodes. To the best of our knowledge, these are the largest USAHLP problem dimensions solved in the literature until now. In addition, in this paper, we report for the first time optimal solutions for 30 AP and modified AP instances.     

A transgenetic algorithm applied to the Traveling Car Renter Problem

The Traveling Car Renter is a new problem and constitutes a generalization of the Traveling Salesman. Several applications arise from it, mainly in the scheduling optimization of rental cars and in other problems concerning transport systems. In this paper, an integer quadratic programming model is presented for the Traveling Car Renter. It is linearized and implemented on a solver providing optimal solutions to a set of eighteen small instances. Large instances are tackled with a transgenetic algorithm proposed here. A computational experiment is reported on sixty instances and the proposed algorithm is compared to a memetic algorithm presented previously. The computational experiment aimed at focusing on the differences of performance between the two heuristic algorithms due to the search strategy used by each of them. Therefore, the implementation of both methods shared several elements. The results show that the transgenetic algorithm presents the best performance, indicating that its cooperative evolutionary process was more effective on the investigated problem than the competitive scheme of the memetic algorithm.     

A memetic algorithm for the re-entrant permutation flowshop scheduling problem to minimize the makespan

A common assumption in the classical permutation flowshop scheduling model is that each job is processed on each machine at most once. However, this assumption does not hold for a re-entrant flowshop in which a job may be operated by one or more machines many times. Given that the re-entrant permutation flowshop scheduling problem to minimize the makespan is very complex, we adopt the CPLEX solver and develop a memetic algorithm (MA) to tackle the problem. We conduct computational experiments to test the effectiveness of the proposed algorithm and compare it with two existing heuristics. The results show that CPLEX can solve mid-size problem instances in a reasonable computing time, and the proposed MA is effective in treating the problem and outperforms the two existing heuristics.     

A memetic algorithm-based heuristic for a scheduling problem in printed circuit board assembly

This research develops a memetic algorithm to solve Printed Circuit Board (PCB) scheduling with sequence-dependent setup times on a single machine with constrained feeder capacity. The objective of the scheduling problem is to minimize the total weighted tardiness. A memetic algorithm-based heuristics is developed by integrating a genetic algorithm, Minimum Slack Time (MST) scheduling rule, “Keep Tool Needed Soonest” (KTNS) policy, and a local search procedure. Application of the MA results in two outcome plans: a scheduling plan and a feeder setup plan.Numerical experiments show that compared to a number of commonly used dispatching rules, the memetic algorithm provides better solutions in term of minimum total weighted tardiness. Even the computation is the highest, it still practical. Calibration of MA parameter values is also explored in this study.     

Data-Driven Heuristic Assisted Memetic Algorithm for Efficient Inter-Satellite Link Scheduling in the BeiDou Navigation Satellite System

Inter-satellite link (ISL) scheduling is required by the BeiDou Navigation Satellite System (BDS) to guarantee the system ranging and communication performance. In the BDS, a great number of ISL scheduling instances must be addressed every day, which will certainly spend a lot of time via normal metaheuristics and hardly meet the quick-response requirements that often occur in real-world applications. To address the dual requirements of normal and quick-response ISL schedulings, a data-driven heuristic assisted memetic algorithm (DHMA) is proposed in this paper, which includes a high-performance memetic algorithm (MA) and a data-driven heuristic. In normal situations, the high-performance MA that hybridizes parallelism, competition, and evolution strategies is performed for high-quality ISL scheduling solutions over time. When in quick-response situations, the data-driven heuristic is performed to quickly schedule high-probability ISLs according to a prediction model, which is trained from the high-quality MA solutions. The main idea of the DHMA is to address normal and quick-response schedulings separately, while high-quality normal scheduling data are trained for quick-response use. In addition, this paper also presents an easy-to-understand ISL scheduling model and its NP-completeness. A seven-day experimental study with 10 080 one-minute ISL scheduling instances shows the efficient performance of the DHMA in addressing the ISL scheduling in normal (in 84 hours) and quick-response (in 0.62 hour) situations, which can well meet the dual scheduling requirements in real-world BDS applications.      

A hybrid memetic algorithm for maximizing the weighted number of just-in-time jobs on unrelated parallel machines

This paper presents a hybrid memetic algorithm for the problem of scheduling n jobs on m unrelated parallel machines with the objective of maximizing the weighted number of jobs that are completed exactly at their due dates. For each job, due date, weight, and the processing times on different machines are given. It has been shown that when the numbers of machines are a part of input, this problem is NP-hard in the strong sense. At first, the problem is formulated as an integer linear programming model. This model is practical to solve small-size problems. Afterward, a hybrid memetic algorithm is implemented which uses two heuristic algorithms as constructive algorithms, making initial population set. A data oriented mutation operator is implemented so as to facilitate memetic algorithm search process. Performance of all algorithms including heuristics (H1 and H2), hybrid genetic algorithm and hybrid memetic algorithm are evaluated through computational experiments which showed the capabilities of the proposed hybrid algorithm.     

Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework

This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the univariate feature ranking information. This empirical study on commonly used data sets from the University of California, Irvine repository and microarray data sets shows that the proposed method outperforms existing methods in terms of classification accuracy, number of selected features, and computational efficiency. Furthermore, we investigate several major issues of memetic algorithm (MA) to identify a good balance between local search and genetic search so as to maximize search quality and efficiency in the hybrid filter and wrapper MA     

A parallel sort merge join algorithm for managing data skew

A parallel sort-merge-join algorithm which uses a divide-and-conquer approach to address the data skew problem is proposed. The proposed algorithm adds an extra, low-cost scheduling phase to the usual sort, transfer, and join phases. During the scheduling phase, a parallelizable optimization algorithm, using the output of the sort phase, attempts to balance the load across the multiple processors in the subsequent join phase. The algorithm naturally identifies the largest skew elements, and assigns each of them to an optimal number of processors. Assuming a Zipf-like distribution of data skew, the algorithm is demonstrated to achieve very good load balancing for the join phase, and is shown to be very robust relative, among other things, to the degree of data skew and the total number of processors     

Wrapper-filter feature selection algorithm using a memetic framework.

This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the univariate feature ranking information. This empirical study on commonly used data sets from the University of California, Irvine repository and microarray data sets shows that the proposed method outperforms existing methods in terms of classification accuracy, number of selected features, and computational efficiency. Furthermore, we investigate several major issues of memetic algorithm (MA) to identify a good balance between local search and genetic search so as to maximize search quality and efficiency in the hybrid filter and wrapper MA.     

Solving the facility location and design (1∣1)-centroid problem via parallel algorithms

Several parallel strategies for solving a centroid problem are presented. In the competitive location problem considered in this paper, the aim is to maximize the profit obtained by a chain (the leader) knowing that a competitor (the follower) will react by locating another single facility after the leader locates its own facility. A global optimization memetic algorithm called UEGO_cent.SASS was proposed to cope with this hard-to-solve optimization problem. Now, five parallel implementations of the optimization algorithm have been developed. The use of several processors, and hence more computational resources, allows us to solve bigger problems and to implement new methods which increase the robustness of the algorithm at finding the global optimum. A computational study comparing the new parallel methods in terms of efficiency and effectiveness has been carried out.     

Analyzing Scalability of Parallel Algorithms and Architectures

The scalability of a parallel algorithm on a parallel architecture is a measure of its capacity to effectively utilize an increasing number of processors. Scalability analysis may be used to select the best algorithm-architecture combination for a problem under different constraints on the growth of the problem size and the number of processors. It may be used to predict the performance of a parallel algorithm and a parallel architecture for a large number of processors from the known performance on fewer processors. For a fixed problem size, it may be used to determine the optimal number of processors to be used and the maximum possible speedup that can be obtained. The objectives of this paper are to critically assess the state of the art in the theory of scalability analysis, and to motivate further research on the development of new and more comprehensive analytical tools to study the scalability of parallel algorithms and architectures. We survey a number of techniques and formalisms that have been developed for studying scalability issues, and discuss their interrelationships. For example, we derive an important relationship between time-constrained scaling and the isoefficiency function. We point out some of the weaknesses of the existing schemes for measuring scalability, and discuss possible ways of extending them.     

A hypercube algorithm for the 0/1 Knapsack problem

Many combinatorial optimization problems are known to be NP-complete. A common point of view is that finding fast algorithms for such problems using polynomial number of processors is unlikely. However, facts of this kind usually are established for “worst” case situations and in practice many instances of NP-complete problems are successfully solved in polynomial time by such traditional combinatorial optimization techniques as dynamic programming and branch-and-bound. New opportunities for effective solution of combinatorial problems emerged with the advent of parallel machines. In this paper we describe an algorithm which generates an optimal solution for the 0/1 integer Knapsack problem on the NCUBE hypercube computer. It is also demonstrated that the same algorithm can be applied for the two-dimensional 0/1 Knapsack problem. Experimental data which support the theoretical claims are provided for large instances of the one- and two-dimensional Knapsack problems.     

A memetic algorithm for the capacitated m-ring-star problem

The Capacitated m-Ring-Star Problem (CmRSP) models a network topology design problem in the telecommunications industry. In this paper, we propose to solve this problem using a memetic algorithm that includes a crossover operation, a mutation operation, a local search involving three neighborhood operators, and a population selection strategy that maintains population diversity. Our approach generates the best known solutions for 131 out of 138 benchmark instances, improving on the previous best solutions for 24 of them, and exhibits more advantages on large benchmark instances when compared with the best existing approach. Additionally, all existing algorithms for this problem in literature assume that the underlying graph of the problem instance satisfies the triangle inequality rule; our approach does not require this assumption. We also generated a new set of 36 larger test instances based on a digital data service network price structure to serve as a new benchmark data set for future researchers.     

Genetic scheduling for parallel processor systems: comparativestudies and performance issues

Task scheduling is essential for the proper functioning of parallel processor systems. Scheduling of tasks onto networks of parallel processors is an interesting problem that is well-defined and documented in the literature. However, most of the available techniques are based on heuristics that solve certain instances of the scheduling problem very efficiently and in reasonable amounts of time. This paper investigates an alternative paradigm, based on genetic algorithms, to efficiently solve the scheduling problem without the need to apply any restricted assumptions that are problem-specific, such is the case when using heuristics. Genetic algorithms are powerful search techniques based on the principles of evolution and natural selection. The performance of the genetic approach will be compared to the well-known list scheduling heuristics. The conditions under which a genetic algorithm performs best will also be highlighted. This will be accompanied by a number of examples and case studies     

Performance Measures for Evaluating Algorithms for SIMD Machines

This paper examines measures for evaluating the performance of algorithms for single instruction stream–multiple data stream (SIMD) machines. The SIMD mode of parallelism involves using a large number of processors synchronized together. All processors execute the same instruction at the same time; however, each processor operates on a different data item. The complexity of parallel algorithms is, in general, a function of the machine size (number of processors), problem size, and type of interconnection network used to provide communications among the processors. Measures which quantify the effect of changing the machine-size/problem-size/network-type relationships are therefore needed. A number of such measures are presented and are applied to an example SIMD algorithm from the image processing problem domain. The measures discussed and compared include execution time, speed, parallel efficiency, overhead ratio, processor utilization, redundancy, cost effectiveness, speed-up of the parallel algorithm over the corresponding serial algorithm, and an additive measure called "sprice" which assigns a weighted value to computations and processors.     

A Memetic Approach to the Nurse Rostering Problem

Constructing timetables of work for personnel in healthcare institutions is known to be a highly constrained and difficult problem to solve. In this paper, we discuss a commercial system, together with the model it uses, for this rostering problem. We show that tabu search heuristics can be made effective, particularly for obtaining reasonably good solutions quickly for smaller rostering problems. We discuss the robustness issues, which arise in practice, for tabu search heuristics. This paper introduces a range of new memetic approaches for the problem, which use a steepest descent improvement heuristic within a genetic algorithm framework. We provide empirical evidence to demonstrate the best features of a memetic algorithm for the rostering problem, particularly the nature of an effective recombination operator, and show that these memetic approaches can handle initialisation parameters and a range of instances more robustly than tabu search algorithms, at the expense of longer solution times. Having presented tabu search and memetic approaches (both with benefits and drawbacks) we finally present an algorithm that is a hybrid of both approaches. This technique produces better solutions than either of the earlier approaches and it is relatively unaffected by initialisation and parameter changes, combining some of the best features of each approach to create a hybrid which is greater than the sum of its component algorithms.     

A case study of memetic algorithms for constraint optimization

There is a variety of knapsack problems in the literature. Multidimensional 0–1 knapsack problem (MKP) is an NP-hard combinatorial optimization problem having many application areas. Many approaches have been proposed for solving this problem. In this paper, an empirical investigation of memetic algorithms (MAs) that hybridize genetic algorithms (GAs) with hill climbing for solving MKPs is provided. Two distinct sets of experiments are performed. During the initial experiments, MA parameters are tuned. GA and four MAs each using a different hill climbing method based on the same configuration are evaluated. In the second set of experiments, a self-adaptive (co-evolving) multimeme memetic algorithm (MMA) is compared to the best MA from the parameter tuning experiments. MMA utilizes the evolutionary process as a learning mechanism for choosing the appropriate hill climbing method to improve a candidate solution at a given time. Two well-known MKP benchmarks are used during the experiments.