Wei-Chang Yeh

Using multi-objective genetic algorithm for partner selection in green supply chain problems

Partner selection is an important issue in the supply chain management. Since environment protection has been of concern to public in recent years, and the traditional supplier selection did not consider about this factor; therefore, this paper introduced green criteria into the framework of supplier selection criteria. The aim of this research was to develop an optimum mathematical planning model for green partner selection, which involved four objectives such as cost, time, product quality and green appraisal score. In order to solve these conflicting objectives, we adopted two multi-objective genetic algorithms to find the set of Pareto-optimal solutions, which utilized the weighted sum approach that can generate more number of solutions. In experimental analysis, we introduced a {4,4,4,4} supply chain network structure, and compared average number Pareto-optimal solutions and CPU times of two algorithms.

Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm

This study presents an integrated system where wavelet transforms and recurrent neural network (RNN) based on artificial bee colony (abc) algorithm (called ABC-RNN) are combined for stock price forecasting. The system comprises three stages. First, the wavelet transform using the Haar wavelet is applied to decompose the stock price time series and thus eliminate noise. Second, the RNN, which has a simple architecture and uses numerous fundamental and technical indicators, is applied to construct the input features chosen via Stepwise Regression-Correlation Selection (SRCS). Third, the Artificial Bee Colony algorithm (ABC) is utilized to optimize the RNN weights and biases under a parameter space design. For illustration and evaluation purposes, this study refers to the simulation results of several international stock markets, including the Dow Jones Industrial Average Index (DJIA), London FTSE-100 Index (FTSE), Tokyo Nikkei-225 Index (Nikkei), and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). As these simulation results demonstrate, the proposed system is highly promising and can be implemented in a real-time trading system for forecasting stock prices and maximizing profits.

Solving reliability redundancy allocation problems using an artificial bee colony algorithm

This paper proposed a penalty guided artificial bee colony algorithm (ABC) to solve the reliability redundancy allocation problem (RAP). The redundancy allocation problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint, e.g. the total cost. RAP has been an active area of research for the past four decades. The difficulty that one is confronted with the RAP is the maintenance of feasibility with respect to three nonlinear constraints, namely, cost, weight and volume related constraints. In this paper nonlinearly mixed-integer reliability design problems are investigated where both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of the system. The reliability design problems have been studied in the literature for decades, usually using mathematical programming or heuristic optimization approaches. To the best of our knowledge the ABC algorithm can search over promising feasible and infeasible regions to find the feasible optimal/near-optimal solution effectively and efficiently; numerical examples indicate that the proposed approach performs well with the reliability redundant allocation design problems considered in this paper and computational results compare favorably with previously-developed algorithms in the literature.

A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems

Nowadays, the redundancy allocation problem (RAP) is increasingly becoming an important tool in the initial stages of or prior to planning, designing, and control of systems. The multiple multi-level redundancy allocation problem (MMRAP) is an extension of the traditional RAP such that all available items for redundancy (system, module and component) can be simultaneously chosen. In this paper, a novel particle swarm optimization algorithm (PSO) called the two-stage discrete PSO (2DPSO) is presented to solve MMRAP in series systems such that some subsystems or modules consist of different components in series. To the best of our knowledge, this is the first attempt to use a PSO to MMRAP. The proposed PSO used a totally new, very simple, effective and efficient mechanism to move to the next position without velocity. The result obtained by 2DPSO has been compared with those obtained by genetic algorithm (GA) and binary PSO (BPSO). Computational results show that the proposed 2DPSO is very competitive and performs well in the number of times it finds the best solutions, the average numbers of the earliest finding of the best solutions, and computation times.

A Simple Approach To Search For All D-Mcs Of A Limited-Flow Network

Abstract Many real-world systems are multistate systems composed of multistate components in which the reliability can be computed in terms of the lower bound points of level d, called d-MCs. Such systems (electric power, transportation, etc.) may be regarded as flow networks whose arcs have independent, discrete, limited and multivalued random capacities. In this study, all MCs are assumed to be known in advance and we focused on how to find the entire d-MCs before calculating the reliability value of a network. Just based on the definition of d-MC, we develop an intuitive algorithm which is better than the best-known existing method. Analysis of our algorithm and comparison to existing algorithms shows that our proposed method is easier to understand and implement. Finally, the computational complexity of the proposed algorithm is analysed and compared with the existing methods.

A simple minimal path method for estimating the weighted multi-commodity multistate unreliable networks reliability

The weighted multicommodity multistate unreliable network (WMMUN) is a novel network composed of multistate unreliable components (arcs and nodes) capable of transmitting different types of commodities in which capacity weight varies with components. It is an extension of the multistate network. The current method for evaluating the directed WMMUN reliability has been derived from minimal cut (MC) based algorithm. The existing best-known method needed extensive comparison and verification, and failed to find the real directed WMMUN reliability. A very simple algorithm based on minimal paths (MPs) is developed for the WMMUN reliability problem. The correctness and computational complexity of the proposed algorithm will be analyzed and proven. An example is given to illustrate how the WMMUN reliability is evaluated using the proposed algorithm. The relationships among all different versions of MPs are also clarified.

A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method

Breast cancer is one of the leading causes of death among the women in many parts of the world. In 2007, approximately 178,480 women in the United States have been found to have invasive breast cancer. In this paper, we have developed an efficient hybrid data mining approach to separate from a population of patients who have and who do not have breast cancer. The proposed data mining approach has consists of two phases. In first phase, the statistical method will be used to pre-process the data which can eliminate the insignificant features. It can reduce the computational complexity and speed up the data mining process. In second phase, we proposed a new data mining methodology which based on the fundamental concept of the standard particle swarm optimization (PSO) namely discrete PSO. This phase aimed at creating a novel PSO in which each particle was coded in positive integer numbers and has a feasible system structure. Based on the obtained results, our proposed DPSO can improve the accuracy to 98.71%, sensitivity to 100% and specificity to 98.21%. When compared with the previous research, the proposed hybrid approach shows the improvement in both accuracy and robustness. According to the high quality of our research results, the proposed DPSO data mining algorithm can be used as the reference for making decision in hospital and provide the reference for the researchers.

A Simple Algorithm To Search For All D-Mps With Unreliable Nodes

Abstract Many real-world systems are multistate systems composed of multistate components in which the reliability can be computed in terms of the lower bound points of level d, by formulating in terms of either the d-minimal paths (d-MPs) or d-minimal cutsets (d-MCs). Such systems (electric power, transportation, etc.) may be regarded as flow networks whose arcs have independent, discrete, limited and multivalued random capacities. A simple method is proposed to search for all d-MPs for network reliability in a system subject to both arc and node failures. The proposed method does not require re-enumeration for all of the d-MPs or MPs for the additional the node failure consideration. Because only the sum of the flow through into (or from) each unreliable node of the d-MPs which found in the network with perfect nodes is calculated, the proposed algorithm is easier to understand and implement. With considering unreliable nodes, this method is also more realistic and valuable for performing the reliability analysis in an existing network. The computational complexity of the proposed algorithm is analyzed and compared with the existing methods. One example is illustrated to show how all d-MPs are generated in a network with arc and node failures solved by the proposed algorithm.

Feature selection with Intelligent Dynamic Swarm and Rough Set

Data mining is the most commonly used name to solve problems by analyzing data already present in databases. Feature selection is an important problem in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets. Many approaches, methods and goals including Genetic Algorithms (GA) and swarm-based approaches have been tried out for feature selection in order to these goals. Furthermore, a new technique which named Particle Swarm Optimization (PSO) has been proved to be competitive with GA in several tasks, mainly in optimization areas. However, there are some shortcomings in PSO such as premature convergence. To overcome these, we propose a new evolutionary algorithm called Intelligent Dynamic Swarm (IDS) that is a modified Particle Swarm Optimization. Experimental results states competitive performance of IDS. Due to less computing for swarm generation, averagely IDS is over 30% faster than traditional PSO.

A Particle Swarm Optimization Approach Based on Monte Carlo Simulation for Solving the Complex Network Reliability Problem

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.