251 research outputs found
Coevolutionary optimization of fuzzy logic intelligence for strategic decision support
©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.We present a description and initial results of a computer code that coevolves fuzzy logic rules to play a two-sided zero-sum competitive game. It is based on the TEMPO Military Planning Game that has been used to teach resource allocation to over 20 000 students over the past 40 years. No feasible algorithm for optimal play is known. The coevolved rules, when pitted against human players, usually win the first few competitions. For reasons not yet understood, the evolved rules (found in a symmetrical competition) place little value on information concerning the play of the opponent.Rodney W. Johnson, Michael E. Melich, Zbigniew Michalewicz, and Martin Schmid
A meta-architecture analysis for a coevolved system-of-systems
Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments.
Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity that engineered systems-of-systems exhibit poses challenges to traditional systems engineering approaches. Systems engineers are presented with the problem of understanding how these systems can be designed or adapted given these challenges. Understanding how the environment influences system-of-systems performance allows systems engineers to target the right set of capabilities when adapting the system for improved performance.
This research explores coevolution in a counter-trafficking system-of-systems and develops an approach to demonstrate its impacts. The approach implements a trade study using swing weights to demonstrate the influence of coevolution on stakeholder value, develops a novel future architecture to address degraded capabilities, and demonstrates the impact of the environment on system performance using simulation. The results provide systems engineers with a way to assess the impacts of coevolution on the system-of-systems, identify those capabilities most affected, and explore alternative meta-architectures to improve system-of-systems performance in new environments --Abstract, page iii
Modeling of Biological Intelligence for SCM System Optimization
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms
Initialization of a Multi-objective Evolutionary Algorithms Knowledge Acquisition System for Renewable Energy Power Plants
pp. 185-204The design of Renewable Energy Power Plants (REPPs) is crucial not only for the
investments' performance and attractiveness measures, but also for the maximization of
resource (source) usage (e.g. sun, water, and wind) and the minimization of raw
materials (e.g. aluminum: Al, cadmium: Cd, iron: Fe, silicon: Si, and tellurium: Te)
consumption. Hence, several appropriate and satisfactory Multi-objective Problems
(MOPs) are mandatory during the REPPs' design phases. MOPs related tasks can only
be managed by very well organized knowledge acquisition on all REPPs' design
equations and models. The proposed MOPs need to be solved with one or more multiobjective algorithm, such as Multi-objective Evolutionary Algorithms (MOEAs). In this
respect, the first aim of this research study is to start gathering knowledge on the REPPs'
MOPs. The second aim of this study is to gather detailed information about all MOEAs
and available free software tools for their development. The main contribution of this
research is the initialization of a proposed multi-objective evolutionary algorithm
knowledge acquisition system for renewable energy power plants (MOEAs-KAS-FREPPs) (research and development loopwise process: develop, train, validate, improve,
test, improve, operate, and improve). As a simple representative example of this
knowledge acquisition system research with two selective and elective proposed
standard objectives (as test objectives) and eight selective and elective proposed
standard constraints (as test constraints) are generated and applied as a standardized
MOP for a virtual small hydropower plant design and investment. The maximization of
energy generation (MWh) and the minimization of initial investment cost (million €)
are achieved by the Multi-objective Genetic Algorithm (MOGA), the Niched Sharing
Genetic Algorithm/Non-dominated Sorting Genetic Algorithm (NSGA-I), and the
NSGA-II algorithms in the Scilab 6.0.0 as only three standardized MOEAs amongst all
proposed standardized MOEAs on two desktop computer configurations (Windows 10
Home 1709 64 bits, Intel i5-7200 CPU @ 2.7 GHz, 8.00 GB RAM with internet
connection and Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB
RAM with internet connection). The algorithm run-times (computation time) of the
current applications vary between 20.64 and 59.98 seconds.S
A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms
Fuzzy decision making system and the dynamics of business games
Effective and efficient strategic decision making is the backbone for the success of
a business organisation among its competitors in a particular industry. The results
of these decision making processes determine whether the business will continue to
survive or not. In this thesis, fuzzy logic (FL) concepts and game theory are being used
to model strategic decision making processes in business organisations. We generally
modelled competition by business organisations in industries as games where each
business organization is a player. A player formulates his own decisions by making
strategic moves based on uncertain information he has gained about the opponents.
This information relates to prevailing market demand, cost of production, marketing,
consolidation efforts and other business variables. This uncertain information is being
modelled using the concept of fuzzy logic.
In this thesis, simulation experiments were run and results obtained in six different
settings. The first experiment addresses the payoff of the fuzzy player in a typical
duopoly system. The second analyses payoff in an n-player game which was used
to model a perfect market competition with many players. It is an extension of the
two-player game of a duopoly market which we considered in the first experiment.
The third experiment used and analysed real data of companies in a case study. Here,
we chose the competition between Coca-cola and PepsiCo companies who are major
players in the beverage industry. Data were extracted from their published financial
statements to validate our experiment. In the fourth experiment, we modelled
competition in business networks with uncertain information and varying level of
connectivity. We varied the level of interconnections (connectivity) among business
units in the business networks and investigated how missing links affect the payoffs
of players on the networks.
We used the fifth experiment to model business competition as games on boards with
possible constraints or restrictions and varying level of connectivity on the boards.
We also investigated this for games with uncertain information. We varied the level of
interconnections (connectivity) among the nodes on the boards and investigated how
these a ect the payoffs of players that played on the boards. We principally used these
experiments to investigate how the level of availability of vital infrastructures (such
as road networks) in a particular location or region affects profitability of businesses
in that particular region.
The sixth experiment contains simulations in which we introduced the fuzzy game approach
to wage negotiation in managing employers and employees (unions) relationships.
The scheme proposes how employers and employees (unions) can successfully
manage the deadlocks that usually accompany wage negotiations.
In all cases, fuzzy rules are constructed that symbolise various rules and strategic
variables that firms take into consideration before taken decisions. The models also
include learning procedures that enable the agents to optimize these fuzzy rules and
their decision processes. This is the main contribution of the thesis: a set of fuzzy
models that include learning, and can be used to improve decision making in business
The co-evolutionary relationship between digitalization and organizational agility: Ongoing debates, theoretical developments and future research perspectives
This study is the first to provide a systematic review of the literature
focused on the relationship between digitalization and organizational agility
(OA). It applies the bibliographic coupling method to 171 peer-reviewed
contributions published by 30 June 2021. It uses the digitalization perspective
to investigate the enablers, barriers and benefits of processes aimed at
providing firms with the agility required to effectively face increasingly
turbulent environments. Three different, though interconnected, thematic
clusters are discovered and analysed, respectively focusing on big-data
analytic capabilities as crucial drivers of OA, the relationship between
digitalization and agility at a supply chain level, and the role of information
technology capabilities in improving OA. By adopting a dynamic capabilities
perspective, this study overcomes the traditional view, which mainly considers
digital capabilities enablers of OA, rather than as possible outcomes. Our
findings reveal that, in addition to being complex, the relationship between
digitalization and OA has a bidirectional character. This study also identifies
extant research gaps and develops 13 original research propositions on possible
future research pathways and new managerial solutions
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