Lam Thu Bui

Multiobjective optimization for dynamic environments

This paper investigates the use of evolutionary multi-objective optimization methods (EMOs) for solving single-objective optimization problems in dynamic environments. A number of authors proposed the use of EMOs for maintaining diversity in a single objective optimization task, where they transform the single objective optimization problem into a multi-objective optimization problem by adding an artificial objective function. We extend this work by looking at the dynamic single objective task and examine a number of different possibilities for the artificial objective function. We adopt the non-dominated sorting genetic algorithm version 2 (NSGA2). The results show that the resultant formulations are promising and competitive to other methods for handling dynamic environments.

Fitness inheritance for noisy evolutionary multi-objective optimization

This paper compares the performance of anti-noise methods, particularly probabilistic and re-sampling methods, using NSGA2. It then proposes a computationally less expensive approach to counteracting noise using re-sampling and fitness inheritance. Six problems with different difficulties are used to test the methods. The results indicate that the probabilistic approach has better convergence to the Pareto optimal front, but it looses diversity quickly. However, methods based on re-sampling are more robust against noise but they are computationally very expensive to use. The proposed fitness inheritance approach is very competitive to re-sampling methods with much lower computational cost.

Success in Evolutionary Computation

Darwinian evolutionary theory is one of the most important theories in human history for it has equipped us with a valuable tool to understand the amazing world around us. There can be little surprise, therefore, that Evolutionary Computation (EC), inspired by natural evolution, has been so successful in providing high quality solutions in a large number of domains. EC includes a number of techniques, such as Genetic Algorithms, Genetic Programming, Evolution Strategy and Evolutionary Programming, which have been used in a diverse range of highly successful applications. This book brings together some of these EC applications in fields including electronics, telecommunications, health, bioinformatics, supply chain and other engineering domains, to give the audience, including both EC researchers and practitioners, a glimpse of this exciting rapidly evolving field.

Local models--an approach to distributed multi-objective optimization

Abstract When solving real-world optimization problems, evolutionary algorithms often require a large number of fitness evaluations in order to converge to the global optima. Attempts have been made to find techniques to reduce the number of fitness function evaluations. We propose a novel framework in the context of multi-objective optimization where fitness evaluations are distributed by creating a limited number of adaptive spheres spanning the search space. These spheres move towards the global Pareto front as components of a swarm optimization system. We call this process localization. The contribution of the paper is a general framework for distributed evolutionary multi-objective optimization, in which the individuals in each sphere can be controlled by any existing evolutionary multi-objective optimization algorithm in the literature.

Multi-Objective Optimization in Computational Intelligence: Theory and Practice

Multi-Objective Optimization in Computational Intelligence: Theory and Practice explores the theoretical, as well as empirical, performance of MOs on a wide range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. This book provides scholars, academics, and practitioners with a fundamental, comprehensive collection of research on multi-objective optimization techniques, applications, and practices.

Robustness Against the Decision-Maker's Attitude to Risk in Problems With Conflicting Objectives

In multiobjective optimization problems (MOPs), the Pareto set consists of efficient solutions that represent the best trade-offs between the conflicting objectives. Many forms of uncertainty affect the MOP, including uncertainty in the decision variables, parameters or objectives. A source of uncertainty that is not studied in the evolutionary multiobjective optimization (EMO) literature is the decision-makers attitude to risk (DMAR) even though it has great significance in real-world applications. Often the decision-makers change over the course of the decision-making process and thus, some relevant information about preferences of future decision-makers is unknown at the time a decision is made. This poses a major risk to organizations because a new decision-maker may simply reject a decision that has been made previously. When an EMO technique attempts to generate the set of nondominated solutions for a problem, then DMAR-related uncertainty needs to be reduced. Solutions generated by an EMO technique should be robust against perturbations caused by the DMAR. In this paper, we focus on the DMAR as a source of uncertainty and present two new types of robustness in MOP. In the first type, dominance robustness (DR), the robust Pareto solutions are those which, if perturbed, would have a high chance to move to another Pareto solution. In the second type, preference robustness (PR), the robust Pareto solutions are those that are close to each other in configuration space. Dominance robustness captures the ability of a solution to move along the Pareto optimal front under some perturbative variation in the decision space, while PR captures the ability of a solution to produce a smooth transition (in the decision variable space) to its neighbors (defined in the objective space). We propose methods to quantify these robustness concepts, modify existing EMO techniques to capture robustness against the DMAR, and present test problems to examine both DR and PR.

Adaptation in Dynamic Environments: A Case Study in Mission Planning

Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of objectives associated with these plans. All these potential modifications create a huge pressure on planning staff for generating plans that can adapt quickly to changes in environment during execution. In this paper, we address adaptation in dynamic environments. Many researchers in evolutionary community addressed the problem of optimization in dynamic environments. Through an overview on applying evolutionary algorithms for solving dynamic optimization problems, we classify the paper into two main categories: 1) finding/tracking optima, and 2) adaptation and we discuss their relevance for solving planning problems. Based on this discussion, we propose a computational approach to adaptation within the context of planning. This approach models the dynamic planning problem as a multiobjective optimization problem and an evolutionary mechanism is incorporated; this adapts the current solution to new situations when a change occurs. As the multiobjective model is used, the proposed approach produces a set of non-dominated solutions after each planning cycle. This set of solutions can be perceived as an information-rich data set which can be used to support the adaptation process against the effect of changes. The main question is how to exploit this set efficiently. In this paper, we propose a method based on the concept of centroids over a number of changing-time steps; at each step we obtain a set of non-dominated solutions. We carried out a case study on this proposed approach. Mission planning was used for our experiments and experimental analysis. We selected mission planning as our test environment because battlefields are always highly dynamic and uncertain and can be conveniently used to demonstrate different types of changes, especially time-varying constraints. The obtained results support the significance of our centroid-based approach.

A modified strategy for the constriction factor in particle swarm optimization

In this paper, we propose a modification to particle swarm optimization in order to speed up the optimization process. The modification is applied to the constriction coefficient, an important parameter that controls the convergence rate. To validate the proposed strategy, we carried out a number of experiments on a wide range of 25 standard test problems. The obtained results show that the proposed strategy significantly improves the performance of the selected PSO algorithm.

A MULTI-OBJECTIVE RISK-BASED FRAMEWORK FOR MISSION CAPABILITY PLANNING

In this paper, we propose a risk-based framework for military capability planning. Within this framework, metaheuristic techniques such as Evolutionary Algorithms are used to deal with multi-objectivity of a class of NP-hard resource investment problems, called The Mission Capability Planning Problem, under the presence of risk factors. This problem inherently has at least two conflicting objectives: minimizing the cost of investment in the resources as well as the makespan of the plans. The framework allows the addition of a risk-based objective to the problem in order to support risk assessment during the planning process. In other words, with this framework, a mechanism of progressive risk assessment is introduced to capability planning.We analyze the performance of the proposed framework under both scenarios: with and without risk. In the case of no risk, the purpose is to study several optimization-related aspects of the framework such as convergence, trade-off analysis, and its sensitivity to the algorithm parameters; while the second case is to demonstrate the ability of the framework in supporting risk assessment and also robustness analysis.

Diversity as a selection pressure in dynamic environments

Evolutionary algorithms (EAs) are widely used to deal with optimization problems in dynamic environments (DE) [3]. When using EAs to solve DE problems, we are usually interested in the algorithms ability to adapt and recover from the changes. One of the main problems facing an evolutionary method when solving DE problems is the loss of genetic diversity.In this paper, we investigate the use of evolutionary multi-objective optimization methods (EMOs) for single-objective DE problems. For that purpose, we introduce an artificial second objective with the aim to maintain useful diversity in the population. Six different artificial objectives are examined and compared.All the results will be compared against a traditional GA and the random immigrants algorithm[4]. NSGA2 is employed as the evolutionary multi-objective technique.