Integration between Mathematical Programming and Fuzzy Logic to Optimize Consumers Behavior

The concept of utility is used as a decision tool for consumers to choose a commodity. Analyzing consumer behavior is complicated due to the qualitative nature of the utility. Hence, this paper investigates a quantitative method to evaluate the utility of consumers. The methodology is based on linguistic expressions of fuzzy logic. Initially, the authors identify different parameters being effective on utility of a consumer. Then, using fuzzy linguistic expressions the quantitative value of utility are determined. Also some statistical analyses are set to investigate the effective parameters on the utility. Consequently, the optimization is done applying mathematical nonlinear programming. Some analysis is performed as sensitivity study. A case study is conducted to verify the applicability and effectiveness of the proposed methodology.

EvoWebReg

This article presents a complete course registration system through the web (EvoWebReg). The system consists of three parts. The first one is a web application which allows the students to submit their course preferences to the system's database, through Internet. The second part is an administrative tool which controls the whole system and allows its smooth operation, and the third part is an evolutionary algorithm which is responsible for the optimization of the student course schedules according to their submitted preferences, and taking into consideration the constraints imposed by the department. The results of the experimental tests of the evolutionary algorithm prove that our initial objectives to provide an open generic and effective tool, which can satisfactory implement the course registration procedure, were achieved. The proposed system is quite general and can be easily adapted to incorporate the needs of other departments.

Soccer Game Optimization

A new metaheuristic algorithm is proposed. The algorithm integrates the information sharing as well as the evolution operators in the swarm intelligence algorithm and evolutionary algorithm respectively. The basic soccer player movement is used as the analogy to describe the algorithm. The new method has two basic operators; the move off and the move forward. The proposed method elaborates the reproduction process in evolutionary algorithm with the powerful information sharing in the swarm intelligence algorithm. Examples of implementations are provided for continuous and discrete problems. The experiment results reveal that the proposed method has the potential to become a powerful optimization method. As a new method, the proposed algorithm can be enhanced in many different ways such as investigating the parameter setting, elaborating more aspects of the soccer player movement as well as implementing the proposed method to solve various optimization problems.

Soft Methods for Automatic Drug Infusion in Medical Care Environment

The automatic drug infusion in medical care environment remains an elusive goal due to the inherent specificities of the biological systems under control and to subtle shortcomings of the current models. The central aim of this chapter is to present an overview of soft computing techniques and systems that can be used to ameliorate those problems. The applications of control systems in modern medicine are discussed along with several enabling methodologies. The advantages and limitations of automatic drug infusion systems are analyzed. In order to comprehend the evolution of these systems and identify recent advances and research trends, a survey on the hypertension control problem is provided. For illustration, a state-of-the-art automatic drug infusion controller of Sodium Nitroprusside for the mean arterial pressure is described in detail. The chapter ends with final remarks on future research directions towards a fully automated drug infusion system.

Application of Meta-Heuristic Optimization Algorithms in Electric Power Systems

Optimization of solutions on expansion of electric power systems (EPS) and their control plays a crucial part in ensuring efficiency of the power industry, reliability of electric power supply to consumers and power quality. Until recently, this goal was accomplished by applying classical and modern methods of linear and nonlinear programming. In some complicated cases, however, these methods turn out to be rather inefficient. Meta-heuristic optimization algorithms often make it possible to successfully cope with arising difficulties. State estimation (SE) is used to calculate current operating conditions of EPS using the SCADA measurements of state variables (voltages, currents etc.). To solve the SE problem, the Energy Systems Institute of Siberian Branch of Russian Academy of Sciences (ESI of SB RAS) has devised a method based on test equations (TE), i.e. on the steady state equations that contain only measured parameters. Here, a technique for EPS SE using genetic algorithms (GA) is suggested. SE is the main tool for EPS monitoring. The quality of SE results determines largely the EPS control efficiency. An algorithm for exclusion of wrong SE calculations is described. The algorithm using artificial neural networks (ANN) is based on the analysis of results of the calculation performed solving the SE problem with different combinations of constants. The proposed procedure is checked on real data.

A Particle Swarm Optimizer for Constrained Multiobjective Optimization

Generally, constraint-handling techniques are designed for evolutionary algorithms to solve Constrained Multiobjective Optimization Problems (CMOPs). Most Multiojective Particle Swarm Optimization (MOPSO) designs adopt these existing constraint-handling techniques to deal with CMOPs. In this chapter, the authors present a constrained MOPSO in which the information related to particles' infeasibility and feasibility status is utilized effectively to guide the particles to search for feasible solutions and to improve the quality of the optimal solution found. The updating of personal best archive is based on the particles' Pareto ranks and their constraint violations. The infeasible global best archive is adopted to store infeasible nondominated solutions. The acceleration constants are adjusted depending on the personal bests' and selected global bests' infeasibility and feasibility statuses. The personal bests' feasibility statuses are integrated to estimate the mutation rate in the mutation procedure. The simulation results indicate that the proposed constrained MOPSO is highly competitive in solving selected benchmark problems.

A Fuzzy Cyber-Risk Analysis Model for Assessing Attacks on the Availability and Integrity of the Military Command and Control Systems

The increasing complexity in Military Command and Control (C2) systems has led to greater vulnerability due to system availability and integrity caused by internal vulnerabilities and external threats. Several studies have proposed measures of availability and integrity for the assets in the C2 systems using precise and certain measures (i.e., the exact number of attacks on the availability and the integrity, the number of countermeasures for the availability and integrity attacks, the effectiveness of the availability and integrity countermeasure in eliminating the threats, and the financial impact of each attack on the availability and integrity of the assets). However, these measures are often uncertain in real-world problems. The source of uncertainty can be vagueness or ambiguity. Fuzzy logic and fuzzy sets can represent vagueness and ambiguity by formalizing inaccuracies inherent in human decision-making. In this paper, the authors extend the risk assessment literature by including fuzzy measures for the number of attacks on the availability and the integrity, the number of countermeasures for the availability and integrity attacks, and the effectiveness of the availability and integrity countermeasure in eliminating these threats. They analyze the financial impact of each attack on the availability and integrity of the assets and propose a comprehensive cyber-risk assessment system for the Military C2 in the fuzzy environment.

Genetic Algorithms Quality Assessment Implementing Intuitionistic Fuzzy Logic

Intuitionistic fuzzy logic has been implemented in this investigation aiming to derive intuitionistic fuzzy estimations of model parameters of yeast fed-batch cultivation. Considered here are standard simple and multi-population genetic algorithms as well as their modifications differ from each other in execution order of main genetic operators (selection, crossover, and mutation). All are applied for the purpose of parameter identification of S. cerevisiae fed-batch cultivation. Performances of the examined algorithms have been assessed before and after the application of a procedure for narrowing the range of model parameters variation. Behavior of standard simple genetic algorithm has been also examined for different values of proof as the most sensitive genetic algorithms parameter toward convergence time, namely, generation gap (GGAP). Results obtained after the intuitionistic fuzzy logic implementation for assessment of genetic algorithms performance have been compared. As a result, the most reliable algorithm/value of GGAP ensuring the fastest and the most valuable solution is distinguished.

Hybrid Evolutionary Optimization Algorithms

The novel industrial manufacturing sector inevitably faces problems of uncertainty in various aspects such as raw material availability, human resource availability, processing capability and constraints and limitations imposed by the marketing department. These problems have to be solved by a methodology which takes care of such unexpected information. As the analyst faces this man made chaotic and due to natural disaster problems, the decision maker and the implementer have to work collaboratively with the analyst for taking up a decision on an innovative strategy for implementation. Such complex problems of vagueness and uncertainty can be handled by the hybrid evolutionary intelligence algorithms. In this chapter, a new hybrid evolutionary optimization based methodology using a specific non-linear membership function, named as modified S-curve membership function, is proposed. The modified S-curve membership function is first formulated and its flexibility in taking up vagueness in parameters is established by an analytical approach. This membership function is applied for its useful performance through industrial production problems by employing hybrid evolutionary optimization algorithms. The novelty and the originality of this non-linear S-curve membership function are further established using a real life industrial production planning of an industrial manufacturing sector. The unit produces 8 products using 8 raw materials, mixed in various proportions by 9 different processes under 29 constraints. This complex problem has a cubic non-linear objective function. Comprehensive solutions to a non-linear real world objective function are achieved thus establishing the usefulness of the realistic membership function for decision making in industrial production planning.

SOSPD Controllers Tuning by Means of an Evolutionary Algorithm

The Proportional Integral Derivative (PID) controller is the most widely used industrial device to monitoring and controlling processes. There are numerous methods for estimating the controller parameters, in general, resolving particular cases. Current trends in parameter estimation minimize an integral performance criterion. Therefore, the calculation of the controller parameters is proposed as an optimization problem. Although there are alternatives to the traditional rules of tuning, there is not yet a study showing that the use of heuristic algorithms it is indeed better than using the classic methods of optimal tuning. In this paper, the evolutionary algorithm MAGO is used as a tool to optimize the controller parameters. The procedure is applied to a range of standard plants modeled as a Second Order System plus Time Delay. Better results than traditional methods of optimal tuning, regardless of the operating mode of the controller, are yielded.