An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Dreżewski, Rafał; Doroz, Krzysztof

doi:10.3390/sym9090168

Cited by 26 publications

(18 citation statements)

References 62 publications

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“…In the context of increased demand for a higher degree of product customization and personalization, simulation leads to resource economy [28][29][30]. Manufacturing processes simulation, as an intermediate stage in implementing digital manufacturing asks for the use of specific technologies and methods [31][32][33][34][35][36][37][38][39].…”

Section: Production Process Simulationmentioning

confidence: 99%

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Agent-Based Simulation of Value Flow in an Industrial Production Process

et al. 2019

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The current competitive environment demands companies to be more and more efficient. In order to increase manufacturing efficiency, two apparently independent approaches have emerged: lean strategies, focusing on identifying and minimizing non-added value activities, identifying wastes in the system and their elimination, and information tools for planning and controlling activities. In this paper, a manufacturing system was considered for which it was necessary to increase the production capacity in order to respond to the customer’s increased demand. A practical case study in the automotive industry for a medium-sized enterprise was considered. In order to investigate the production process parameters and to implement lean principles, Value Stream Mapping (current analysis and optimized solution) and Anylogic agent-based simulations were carried out. Based on this, the lean performances, specific for the target VSM, were evaluated in terms of key performance indicators. The benefits of integrating agent-based simulation in the design and analysis of the value flow in the production chain are the capitalization of the information offered by Value Stream Mapping and the possibility to choose the best one from the possible scenarios. It generates important time and cost reductions without further resource waste.

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Section: Production Process Simulationmentioning

confidence: 99%

“…Agents may model objects of very diverse nature and scale and allow defining the agents' behavior which will prove useful for more complex research. The agent-based approach is more general and powerful [31,32,40,41]; it is also easier to implement and maintain [40].…”

Section: Production Process Simulationmentioning

confidence: 99%

Agent-Based Simulation of Value Flow in an Industrial Production Process

et al. 2019

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“…It should be noted that the theoretical background of evolutionary methods is yet to be defined. Nonetheless, genetic algorithms have benefited from a proven record of identifying original solutions to classical problems [41,42]. With respect to regression and VAR estimation, we must mention the recent work of References [32,35].…”

Section: Genetic Varmentioning

confidence: 99%

“…Likelihood of the model parameters given the data [52] Higher is better Number of estimated parameters Lower is better Mean of squared errors for forecasting 5% of the dataset Lower is better Akaike information criterion [41] Lower is better Likelihood of the model parameters given the data [42] Lower is better…”

Section: Criterion Source Decisionmentioning

confidence: 99%

Conditional Granger Causality and Genetic Algorithms in VAR Model Selection

Marica

Horobeț

2019

Symmetry

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Overcoming symmetry in combinatorial evolutionary algorithms is a challenge for existing niching methods. This research presents a genetic algorithm designed for the shrinkage of the coefficient matrix in vector autoregression (VAR) models, constructed on two pillars: conditional Granger causality and Lasso regression. Departing from a recent information theory proof that Granger causality and transfer entropy are equivalent, we propose a heuristic method for the identification of true structural dependencies in multivariate economic time series. Through rigorous testing, both empirically and through simulations, the present paper proves that genetic algorithms initialized with classical solutions are able to easily break the symmetry of random search and progress towards specific modeling.

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“…The aim of any multi-objective optimization algorithm is to search for a set of solutions that manages the trade-offs among a set of conflicting optimization features, such as minimization and maximization features [33]. In addition, multi-objective optimization algorithms help to determine an unconstrained maxima or minima, and the optimal solution of continuous or differentiable objective functions [34].…”

Section: Multi-objective Optimization Algorithmsmentioning

confidence: 99%

The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

et al. 2018

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Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.

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An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Cited by 26 publications

References 62 publications

Agent-Based Simulation of Value Flow in an Industrial Production Process

Agent-Based Simulation of Value Flow in an Industrial Production Process

Conditional Granger Causality and Genetic Algorithms in VAR Model Selection

The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

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