IMPLEMENTASI ALGORITMA FIREFLY PADA MASALAH OPTIMASI PORTOFOLIO

Penelitian ini bertujuan untuk mengoptimalkan portofolio saham dengan menggunakan Algoritma Firefly dan model multi-objektif mean-varian. Penelitian ini dilakukan dengan menggunakan data yang dipublikasikan oleh Bursa Efek Indonesia (BEI) untuk 16 saham terpilih, yang diseleksi berdasarkan sektor dan tingkat keaktifannya. Dalam upaya mencapai portofolio optimal, penelitian ini menggunakan pendekatan multi-objektif, dengan mempertimbangkan dua kriteria utama, yaitu tingkat risiko dan tingkat pengembalian saham. Algoritma Firefly digunakan sebagai metode optimasi untuk mencari kombinasi bobot optimal dari saham-saham yang ada dalam portofolio. Algoritma Firefly merupakan metode optimasi berbasis populasi yang terinspirasi oleh perilaku koloni kunang-kunang. Setiap Firefly mewakili sebuah solusi potensial (kombinasi bobot saham dalam portofolio). Firefly yang memiliki nilai fungsi tujuan (tingkat risiko dan tingkat pengembalian) yang lebih baik cenderung menarik Firefly lainnya menuju arahnya. Penelitian ini menghasilkan sebuah portofolio efisien, yang merupakan kombinasi optimal dari bobot saham dengan tingkat risiko dan tingkat pengembalian yang berbeda. Semakin tinggi tingkat pengembalian yang diharapkan, maka semakin tinggi pula tingkat risiko yang harus ditanggung. This study aims to optimize the stock portfolio using Firefly Algorithm and mean-variance multi-objective model. This research was conducted using data published by the Indonesia Stock Exchange (IDX) for 16 selected stocks, which were selected based on their sector and level of activity. In an effort to achieve an optimal portfolio, this research uses a multi-objective approach, considering two main criteria, namely the level of risk and the level of stock returns. Firefly algorithm is used as an optimization method to find the optimal weight combination of stocks in the portfolio. The Firefly algorithm is a population-based optimization method inspired by the behavior of firefly colonies. Each Firefly represents a potential solution (the combination of stock weights in the portfolio). A Firefly that has a better objective function value (risk and return) tends to attract other Fireflys towards it. This research results in an efficient portfolio, which is an optimal combination of stock weights with different levels of risk and return. The higher the expected rate of return, the higher the level of risk that must be borne

Differential Evolution: A Survey and Analysis

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.http://dx.doi.org/10.3390/app810194

A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking

Middle East and North Africa (MENA) countries present a banking industry that is well-known for regulatory and cultural heterogeneity, besides ownership, origin, and type diversity. This paper explores these issues by developing a Dynamic Network DEA model in order to handle the underlying relationships among major accounting and financial indicators. Firstly, a relational model encompassing major profit sheet, balance sheet, and financial health indicators is presented under a dynamic network structure. Subsequently, the dynamic effect of carry-over indicators is incorporated into it so that efficiency scores can be properly computed for these three substructures. The impact of contextual variables related to bank ownership, its type, and whether or not it has undergone a previous merger and acquisition process is tested by means of a stochastic non-linear model solved by differential evolution, which combines bootstrapped Simplex, Tobit, Beta, and Simar and Wilson truncated regression results. The results reveal that bank type, origin, and ownership impact efficiency levels differently in terms of profit sheet, balance sheet, and financial health indicators, although the impact of culture and regulatory barriers seem to prevail at the country level

Efficient Algorithms for Solving Size-Shape-Topology Truss Optimization and Shortest Path Problems

Efficient numerical algorithms for solving structural and Shortest Path (SP) problems are proposed and explained in this study. A variant of the Differential Evolution (DE) algorithm for optimal (minimum) design of 2-D and 3-D truss structures is proposed. This proposed DE algorithm can handle size-shape-topology structural optimization. The design variables can be mixed continuous, integer/or discrete values. Constraints are nodal displacement, element stresses and buckling limitations. For dynamic (time dependent) networks, two additional algorithms are also proposed in this study. A heuristic algorithm to find the departure time (at a specified source node) for a given (or specified) arrival time (at a specified destination node) of a given dynamic network. Finally, an efficient bidirectional Dijkstra shortest path (SP) heuristic algorithm is also proposed. Extensive numerical examples have been conducted in this study to validate the effectiveness and the robustness of the proposed three numerical algorithms

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

PAC:MAN: sistema de gestão do risco de acidentes de poluição em zonas costeiras

Tese de mestrado em Engenharia Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012O efeito da poluição por derrames acidentais nos ecossistemas costeiros motivou a procura e o desenvolvimento de abordagens para planeamento e resposta atempados à emergência com o intuito de proteger os recursos aquáticos. Os sistemas de monitorização da poluição e de modelação existentes são utilizados de forma independente durante acidentes deste âmbito sem a eficácia pretendida. A prevenção do risco de derrame é, habitualmente, feita via planos de contingência com base em estudos simplistas não refletindo o dinamismo da informação nem permitindo o alerta atempado dos gestores costeiros devido ao uso de tecnologia desatualizada. Os sistemas de gestão de risco, testados com sucesso em desastres ambientais e humanitários, demonstram ser soluções promissoras. A sua adequação permite criar sistemas de gestão de risco mais específicos, como riscos de poluição e gestão da resposta à emergência em zonas costeiras. Esta inovação permite conjugar a modelação costeira de vanguarda para análise de risco, a riqueza de informação ambiental existente para a definição de indicadores de condições propícias à ocorrência de derrames e as tecnologias de comunicação. Obtém-se como resultado um conjunto de meios de alerta precoce e resposta mais eficiente e benéfica do ponto de vista da segurança das populações, da capacidade de atuação dos gestores costeiros e da manutenção dos recursos naturais costeiros. A adaptação dos módulos do sistema de gestão de risco de acidentes por rotura de barragens SAGE-B permitiu conceber um novo sistema de gestão de risco de poluição em zonas costeiras que incluiu um sistema de alerta precoce resultante da aplicação dos modelos, um sistema de aviso associado e uma base de dados com os recursos em risco e os meios de resposta à emergência para a análise da vulnerabilidade na Ria de Aveiro, obtendo-se uma nova metodologia genérica de planeamento e resposta para riscos de poluição costeira.The effect of pollution by accidental spills on coastal ecosystems motivated the search and the development of solutions to emergency planning and timely response in order to protect aquatic resources. Nowadays, current pollution monitoring systems and modeling systems are used independently for events of this scope without the desired effectiveness. The risk prevention of oil spill is usually done through contingency plans based on simplistic studies, which do not account for the information dynamics or allow an early warning of coastal managers due to the use of outdated technology. Risk management systems, successfully tested on environmental and humanitarian disasters, prove to be promising solutions. Their adequation allows the creation of more specific risk management systems like pollution risks and management of emergency response in coastal areas. This innovation allows combining the cutting-edge coastal modeling for risk analysis, the richness of existent environmental information to define indicators of conditions prone to the occurrence of oil spills and communication technologies. This results in a set of tools for a more efficient early-warning and response which is also beneficial from the standpoint of security of the populations, from the ability to act from stakeholders and from the maintenance of coastal natural resources. The adaptation of the modules of the dam break accidents risk management system SAGE-B incorporates in a new pollution risk management system for coastal areas, including the early-warning system resulting from the application of the models, the associated alert system and a database of resources at risk and means of emergency response for the analysis of vulnerability in the Aveiro lagoon, proposing a new general methodology for planning and response to risks of coastal pollution