The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them

Real-Time Hand Shape Classification

The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel framework for real-time hand shape classification applicable in real-time applications. We show how the number of gallery images influences the classification accuracy and execution time of the parallel algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Noteworthy, different methods can be used at each step of our parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.Comment: 11 page

Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method

AbstractThe aim of most supply chain optimization problems is to minimize the total cost of the supply chain. However, since environmental protection is of concern to the public, a green supply chain, because of its minimum effect on nature, has been seriously considered as a solution to this concern. This paper addresses the modeling and solving of a supply chain design for annual cost minimization, while considering environmental effects. This paper considers the cost elements of the supply chain, such as transportation, holding and backorder costs, and also, the environmental effect components of the supply chain, such as the amount of NO2, CO and volatile organic particles produced by facilities and transportation in the supply chain. Considering these two components (cost and environmental effects), we propose a multi-objective optimization problem. In this model, the facilities and transportation options have a capacity constraint and, at each level of the chain, we have several transportation options with different costs. We utilize a memetic algorithm in combination with the Taguchi method to solve this complex model. We also propose a novel decoding method and priority based algorithm for coding the solution chromosome. The performance of the proposed solution method has been examined against the hybrid genetic Taguchi algorithm (GATA) on a set of numeric instances, and results indicate that the proposed method can effectively provide better results than previous solution procedures

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

The purpose of this article is to present a novel strategy for the coordinated design of the Thyristor Controlled Series Compensator (TCSC) controller and the Power System Stabilizer (PSS). A time domain objective function that is based on an optimization problem has been defined. This objective function takes into account not only the influence that disturbances have on the mechanical power, but also, and this is more accurately the case, the impact that disturbances have on the reference voltage. When the objective function is minimized, potential disturbances are quickly mitigated, and the deviation of the speed of the generator's rotor is limited; as a result, the system's stability is ultimately improved. Particle Swarm Optimization (PSO) and the Shuffled Frog Leaping Algorithm are both components of a composite strategy that is utilized in the process of determining the optimal controller parameters. (SFLA). An independent controller design as well as a collaborative controller design utilizing PSS and TCSC are developed, which enables a direct evaluation of the functions performed by each. The presentation of the eigenvalue analysis and the findings of the nonlinear simulation can help to provide a better understanding of the efficacy of the outcomes. The findings indicate that the coordinated design is able to successfully damp low-frequency oscillations that are caused by a variety of disturbances, such as changes in the mechanical power input and the setting of the reference voltage, and significantly enhance system stability in power systems that are connected weekly