Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

A flexible integrated forward/reverse logistics model with random path

This dissertation focuses on the structure of a particular logistics network design problem, one that is a major strategic issue for supply chain design and management. Nowadays, the design of the supply chain network must allow for operation at the lowest cost, while providing the best customer service and accounting for environmental protection. Due to business and environmental issues, industrial players are under pressure to take back used products. Moreover, the significance of transportation costs and customer satisfaction spurs an interest in developing a flexible network design model. To this end, in this study, we attempt to include this reverse flow through an integrated design of a forward/reverse supply chain network design, that avoids the sub-optimal solutions derived from separated designs. We formulate a cyclic, seven-stage, logistics network problem as an NP-hard mixed integer linear programming (MILP) model. This integrated, multi-stage model is enriched by using a complete delivery graph in forward flow, which makes the problem more complex. As these kinds of problems belong to the category of NP-hard problems, traditional approaches fail to find an optimal solution in sufficiently short time. Furthermore, considering an integrated design and flexibility at the same time makes the logistics network problem even more complex, and makes it even less likely, if not impossible, for a traditional approach to provide solution within an acceptable time frame. Hence, researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near optimal solutions particularly for large scale test problems. In our case within this thesis, to find a near optimal solution, we apply a Memetic Algorithm with a neighborhood search mechanism and a novel chromosome representation called extended random path direct encoding method which includes two segments. Chromosome representation is one of the main issues that can affect the performance of a Memetic Algorithm. To illustrate the performance of the proposed Memetic Algorithm, LINGO optimization software as commercial package serves as a comparison for small size problems. We show that the proposed algorithm is able to efficiently find a good solution for the flexible, integrated, logistics network. Each algorithm has some parameters that need to be investigated to provide the best performance. In this regard, the effect of different parameters on the behavior of the proposed meta-heuristic algorithm is surveyed first. Then, the Taguchi method is adapted to identify the most important parameters and rank the latter. Additionally, Taguchi method is applied to identify the optimum operating condition of the proposed Memetic Algorithm to improve the results. In this study, four factors that are defined inputs of the proposed Memetic Algorithm, namely: population size, cross over rate, local search iteration, and number of iterations are considered. The analysis of the parameters and the improvement in results are both illustrated by a numerical case studies. Finally, to show the performance of the Memetic Algorithm, a Genetic Algorithm - as a second meta-heuristic algorithm option - is considered as regards large size cases

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Workload Equity in Vehicle Routing Problems: A Survey and Analysis

Over the past two decades, equity aspects have been considered in a growing number of models and methods for vehicle routing problems (VRPs). Equity concerns most often relate to fairly allocating workloads and to balancing the utilization of resources, and many practical applications have been reported in the literature. However, there has been only limited discussion about how workload equity should be modeled in VRPs, and various measures for optimizing such objectives have been proposed and implemented without a critical evaluation of their respective merits and consequences. This article addresses this gap with an analysis of classical and alternative equity functions for biobjective VRP models. In our survey, we review and categorize the existing literature on equitable VRPs. In the analysis, we identify a set of axiomatic properties that an ideal equity measure should satisfy, collect six common measures, and point out important connections between their properties and those of the resulting Pareto-optimal solutions. To gauge the extent of these implications, we also conduct a numerical study on small biobjective VRP instances solvable to optimality. Our study reveals two undesirable consequences when optimizing equity with nonmonotonic functions: Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent, i.e. composed of tours whose workloads are all equal to or longer than those of other Pareto-optimal solutions. We show that the extent of these phenomena should not be underestimated. The results of our biobjective analysis are valid also for weighted sum, constraint-based, or single-objective models. Based on this analysis, we conclude that monotonic equity functions are more appropriate for certain types of VRP models, and suggest promising avenues for further research.Comment: Accepted Manuscrip

A novel clustering methodology based on modularity optimisation for detecting authorship affinities in Shakespearean era plays

© 2016 Naeni et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays