An efficient particle swarm optimizer with application to Man-Day project scheduling problems

The multimode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem. Particle swarm optimization (PSO) has been efficiently applied to the search for near optimal solutions to various NP-hard problems. MRCPSP involves solving two subproblems: mode assignment and activity priority determination. Hence, two PSOs are applied to each subproblem. A constriction PSO is proposed for the activity priority determination while a discrete PSO is employed for mode assignment. A least total resource usage (LTRU) heuristic and minimum slack (MSLK) heuristic ensure better initial solutions. To ensure a diverse initial collection of solutions and thereby enhancing the PSO efficiency, a best heuristic rate (HR) is suggested. Moreover, a new communication topology with random links is also introduced to prevent slow and premature convergence. To verify the performance of the approach, the MRCPSP benchmarks in PSPLIB were evaluated and the results compared to other state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms other algorithms for the MRCPSP problems. Finally, a real-world man-day project scheduling problem (MDPSP)—a MRCPSP problem—was evaluated and the results demonstrate that MDPSP can be solved successfull

Stochastic Greedy-Based Particle Swarm Optimization for Workflow Application in Grid

The workflow application is a common grid application. The objective of a workflow application is to complete all the tasks within the shortest time, i.e., minimal makespan. A job scheduler with a high-efficient scheduling algorithm is required to solve workflow scheduling based on grid information. Scheduling problems are NP-complete problems, which have been well solved by metaheuristic algorithms. To attain effective solutions to workflow application, an algorithm named the stochastic greedy PSO (SGPSO) is proposed to solve workflow scheduling; a new velocity update rule based on stochastic greedy is suggested. Restated, a stochastic greedy-driven search guidance is provided to particles. Meanwhile, a stochastic greedy probability (SGP) parameter is designed to help control whether the search behavior of particles is exploitation or exploration to improve search efficiency. The advantages of the proposed scheme are retaining exploration capability during a search, reducing complexity and computation time, and easy to implement. Retaining exploration capability during a search prevents particles from getting trapped on local optimums. Additionally, the diversity of the proposed SGPSO is verified and analyzed. The experimental results demonstrate that the SGPSO proposed can effectively solve workflow class problems encountered in the grid environment

A Collaborative Model for Calculus Reform-A Preliminary Report

Abstract For the past two decades, both pros and cons of calculus reform have been discussed. A question often asked is, "Has the calculus reform project improved students' understanding of mathematics?" The advocates of the reform movement claim that reform-based calculus may help students gain an intuitive understanding of mathematical propositions and have a better grasp of the real-world applications. Nonetheless, many still question its effect and argue that calculus reform purges calculus of its mathematical rigor and poorly prepares students for advanced mathematical training. East Asian students often rank in the top 10 of TIMSS and PISA. However, out-performing others in an international comparison may not guarantee their success in the learning of calculus. Taiwanese college students usually have a high failure rate in calculus. The National Science Council of Taiwan therefore initiated several projects in 2008 for improving students' learning in calculus. This paper provides a preliminary report on one of the projects, PLEASE, and discusses how it was planned to respond to the tenets of calculus reform movement

Performance Analysis on Competitive, Roulette Wheel and Pseudo-Random Rules for Intrusion Detection

Abstract: Intrusion detection is a critical component of network security; detection schemes fundamentally use the observed characteristics of network packets as a basis for such determinations. Meanwhile, intrusion detection can be regarded as a clustering problem; many clustering schemes have been applied for classifying network packets. Among them, back propagation networks (BPN) and fuzzy c-means (FCM) are popular and well applied. Both of these schemes are based on a competitive characteristic. Nevertheless, a competitive characteristic may cause impropriate clustering results for intrusion detection. Hence, in this study, different clustering criteria are proposed and adopted in BPN and FCM for classifying intrusion packet type; they are the roulette wheel selection rule and pseudo-random rule. Moreover, KDDCUP99 data sets were used as the evaluation packet samples of the experiments, and the given 41 packet features are reduced to 9, 11 and 24 key features for experimentation. Simulation results demonstrate that the proposed intrusion detection criteria applied in BPN yields higher detection rates for the U2R and R2L connections; misclassification of U2R and R2L connections would allow greater damage. Additionally, the suggested roulette wheel selection rule and pseudo-random rule intrusion detection criteria integrated into BPN are superior to other schemes with only 11 features used further reducing complexity and computation time

An efficient particle swarm optimizer with application to Man-Day project scheduling problems

The multimode resource-constrained project scheduling problem (MRCPSP) has been confirmed to be an NP-hard problem. Particle swarm optimization (PSO) has been efficiently applied to the search for near optimal solutions to various NP-hard problems. MRCPSP involves solving two subproblems: mode assignment and activity priority determination. Hence, two PSOs are applied to each subproblem. A constriction PSO is proposed for the activity priority determination while a discrete PSO is employed for mode assignment. A least total resource usage (LTRU) heuristic and minimum slack (MSLK) heuristic ensure better initial solutions. To ensure a diverse initial collection of solutions and thereby enhancing the PSO efficiency, a best heuristic rate (HR) is suggested. Moreover, a new communication topology with random links is also introduced to prevent slow and premature convergence. To verify the performance of the approach, the MRCPSP benchmarks in PSPLIB were evaluated and the results compared to other state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms other algorithms for the MRCPSP problems. Finally, a real-world man-day project scheduling problem (MDPSP)—a MRCPSP problem—was evaluated and the results demonstrate that MDPSP can be solved successfull

Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search

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Dynamic search control-based particle swarm optimization for project scheduling problems

Many machinery manufacturings are categorized as multi-mode resource-constrained project scheduling problems which have attracted significant interest in recent years. It has been shown that such problems are non-deterministic polynomial-time-hard. Particle swarm optimization is one of the most commonly used metaheuristic. Multi-mode resource-constrained project scheduling problems comprise two sub-problems, namely, an activity operating priority and an activity operating mode sub-problems; hence, two particle swarm optimizations are used to solve these two sub-problems. In solving the activity priority sub-problem, a designed global guidance ratio is involved to control the particle’s search behavior. Restated, guiding a diversification search at the beginning stage and conducting an intensification search at latter stage are controlled by adjusting the global guidance ratio. The particle swarm optimization combined with the global guidance ratio mechanism is named global guidance ratio–particle swarm optimization herein. Meanwhile, a non-fixed global guidance ratio adjustment is also suggested to further enhance the search performance. Moreover, different communication topologies for balancing the convergence of using global and local topologies are also suggested in global guidance ratio–particle swarm optimization to further improve the search efficiency. The performance of the proposed global guidance ratio–particle swarm optimization scheme is evaluated by solving all the multi-mode resource-constrained project scheduling problem instances in Project Scheduling Problem Library. It is shown that the scheduling solutions are in good agreement with those presented in the literatures. Hence, the effectiveness of the proposed global guidance ratio–particle swarm optimization scheme is confirmed