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The 0-1 multidimensional knapsack problem (MKP) has been proven it belongs to difficult NP-hard combinatorial optimization problems. There are various search algorithms based on population concept to solve these problems. The particle swarm optimization (PSO) technique is adapted in our study, which proposes a novel PSO algorithm, namely, the binary PSO based on surrogate information with proportional acceleration coefficients (BPSOSIPAC). The proposed algorithm was tested on 135 benchmark problems from the OR-Library to validate and demonstrate the efficiency in solving multidimensional knapsack problems. The results were then compared with those in the other nine existing PSO algorithms. The simulation and evaluation results showed that the proposed algorithm, BPSOSIPAC, is superior to the other methods according to success rate, average number of function evaluations, average number of function evaluations of successful runs, average error (AE), mean absolute deviation, mean absolute percentage error, ...

The multidimensional knapsack problem (MKP) is a difficult combinatorial optimization problem, which has been proven as NP-hard problems. Various population-based search algorithms are applied to solve these problems. The particle swarm optimization (PSO) technique is adapted in our study, which proposes two novel PSO algorithms, namely, the binary PSO with time-varying acceleration coefficients (BPSOTVAC) and the chaotic binary PSO with time-varying acceleration coefficients (CBPSOTVAC). The two proposed methods were tested using 116 benchmark problems from the OR-Library to validate and demonstrate the efficiency of these algorithms in solving multidimensional knapsack problems. The results were then compared with those in the other two existing PSO algorithms. The simulation and evaluation results showed that the proposed algorithms, BPSOTVAC and CBPSOTVAC, are superior over the other methods according to its success rate, mean absolute deviation, mean absolute percentage error, least error, and standard deviation.

Observations from steady-state M/M/1 processes are often used by simulation-methodology researchers conducting Monte Carlo performance evaluations of output analysis, variance reduction, and optimization. The classical method for generating data from M/M/1 processes requires two random number streams. Motivated by the need for variance reduction in simulation experiments, we propose inverse-transformation algorithms for generating M/M/1 processes, including waiting times in queue (M/M/1-QT) and in system (M/M/1-ST), using a single random number stream. Moreover, we propose analytical results for computing the variance of the sample mean from M/M/1 processes. The proposed method for generating processes for the M/M/1-QT and the M/M/1-ST is superior (in terms of statistical effectiveness; i.e., variance reduction) to the corresponding classical methods using two random number streams. Moreover, the proposed generation method for M/M/1-QT is also superior to the classical method in terms of computational efficiency. Finally, although the proposed M/M/1-ST generation method is not as computationally efficient as the classical technique, it does have the same computational efficiency as the classical method if a parallel computing scheme is adopted with the Newton's search method.

Estimating the variance of the sample mean is a classical problem of stochastic simulation. Traditional batch means estimators require specification of the simulation run length a priori. Dynamic batch means (DBM) is a new approach to implement the traditional batch means in fixed memory by dynamically changing both batch size and number of batches without the knowledge of the simulation run length. This article further improves the DBM by considering small storage requirements and fast computation. The proposed algorithm is useful when the simulation run length is random and extremely long in simulation models.

This work combines a Bee Recurrent Neural Network (BRNN) optimized by the Artificial Bee Colony (ABC) algorithm with Monte Carlo Simulation (MCS) to generate a novel approximate model for predicting network reliability. We utilize the Wavelet Transform (WT)-based Latin Hypercube Sampling (LHS) (WLHS) to select input training data, and open the black box of neural networks by constructing a limited space reliability function from neural network parameters. Furthermore, the proposed method compares favorably with existing methods in literature based on experimental results for a benchmark example. The result reveals that the novel WLHS-MCS based on BRNN (WLHS-BRNN-MCS for short) is an excellent estimator of the reliability function.

Reliability optimization has been a popular area of research, and received significant attention due to the critical importance of reliability in various kinds of systems. Most network reliability optimization problems are only focused on solving simple structured networks (e.g., series-parallel networks) of which the reliability function can be easily obtained in advance. However, modern networks are usually very complex, and it is impossible to calculate the exact network reliability function by using traditional analytical methods in limited time. Hence, a new particle swarm optimization (PSO) based on Monte Carlo simulation (MCS), named MCS-PSO, has been proposed to solve complex network reliability optimization problems. The proposed MCS-PSO can minimize cost under reliability constraints. To the best of our knowledge, this is the first attempt to use PSO combined with MCS to solve complex network reliability problems without requiring knowledge of the reliability function in advance. Compared with previous works to solve this problem, the proposed MCS-PSO can have better efficiency by providing a better solution to the complex network reliability optimization problem.