Objective Evolutionary Algorithm Research Articles

Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability.

Multiple objective evolutionary algorithm for temporal linguistic rule extraction

Autonomous temporal linguistic rule extraction is an application of growing interest for its relevance to both decision support systems and fuzzy controllers. In the presented work, rules are evaluated using three qualitative metrics based on their representation on the truth space diagram. Performance metrics are then treated as competing objectives and the multiple objective evolutionary algorithm is used to search for an optimal set of nondominant rules. Novel techniques for data pre-processing and rule set post-processing are designed that deal directly with the delays involved in dynamic systems. Data collected from a simulated hot and cold water mixer are used to validate the proposed procedure.

On the computational efficiency of multiple objective metaheuristics. The knapsack problem case study

The paper describes a computational experiment which goal is to evaluate computational efficiency of three multiple objective evolutionary metaheuristics on the multiple objective multiple constraints knapsack problem. The relative efficiency of the multiple objective algorithms is evaluated with respect to a single objective evolutionary algorithm (EA). We use a methodology that allows consistent evaluation of the quality of approximately Pareto-optimal solutions generated by both multiple and single objective metaheuristics. Then, we compare computational efforts needed to generate solutions of approximately the same quality by the two kinds of methods. The results indicate that computational efficiency of multiple objective EAs deteriorates with the growth of the number of objectives. Furthermore, significant differences in the performance of the three algorithms are observed.

Adaptive representation for single objective optimization

A new technique called Adaptive Representation Evolutionary Algorithm (AREA) is proposed in this paper. AREA involves dynamic alphabets for encoding solutions. The proposed adaptive representation is more compact than binary representation. Genetic operators are usually more aggressive when higher alphabets are used. Therefore the proposed encoding ensures an efficient exploration of the search space. This technique may be used for single and multiobjective optimization. We treat the case of single objective optimization problems in this paper. Despite its simplicity the AREA method is able to generate a population converging towards optimal solutions. Numerical experiments indicate that the AREA technique performs better than other single objective evolutionary algorithms on the considered test functions.

Multi–objective Evolutionary Algorithms for the Risk–return Trade–off in Bank Loan Management

Multi–criteria decision–making is an increasingly accepted tool for decision–making in management. In this work, we highlight the application of a novel multi–objective evolutionary algorithm, NSGA–II, to the risk–return trade–off for a bank–loan portfolio manager. The manager of a bank operating in a competitive environment faces the standard goal of maximizing shareholder wealth. Specifically, this attempts to maximize the net worth of the bank, which in turn involves maximizing the net interest margin of the bank (among other factors, such as non–interest income). At the same time, there are significant regulatory constraints placed on the bank, such as the maintenance of adequate capital, interest–rate risk exposure, etc. The genetic algorithm–based technique used here obtains an approximation to the set of Pareto–optimal solutions which increases the decision flexibility available to the bank manager, and provides a visualization tool for one of the trade–offs involved. The algorithm is also computationally efficient and is contrasted with a traditional multi–objective function — the epsilon–constraint method.