Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms

Path planning is an essential algorithm to help robots complete their task in the field quickly. However, some path planning algorithms are computationally expensive and cannot adapt to new environments with a distinctly different set of obstacles. This paper presents optimal path planning based on a genetic algorithm (GA) that is proposed to be carried out in a dynamic environment with various obstacles. First, the points of the feasible path are found by performing a local search procedure. Then, the points are optimized to find the shortest path. When the optimal path is calculated, the position of the points on the path is smoothed to avoid obstacles in the environment. Thus, the average fitness values and the GA generation are better than the traditional method. The simulation results show that the proposed algorithm successfully finds the optimal path in an environment with multiple obstacles. Compared to a traditional GA-based method, our proposed algorithm has a smoother route due to path optimization. Therefore, this makes the proposed method advantageous in a dynamic environment

Diversity Control in Evolutionary Computation using Asynchronous Dual-Populations with Search Space Partitioning

Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes. Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy

Algoritmos evolutivos adaptativos para problemas de programação de pessoal

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de ProduçãoA crescente concorrência mundial tem estimulado empresas a tornar seus produtos mais competitivos e serviços mais eficazes, observando a redução de custos. Atualmente, percebe-se um rápido crescimento no setor de serviços, o que mostra a importância da utilização eficaz dos recursos materiais e humanos disponíveis. Com o foco neste crescimento, em special no setor de Call Centers, este trabalho aborda uma metodologia para a resolução de problema de Programação de Pessoal com aplicação em uma empresa neste setor. O problema foi dividido em duas etapas, sendo resolvidas na seguinte ordem: problema de Turnos de Trabalho e problema de Designação dos Turnos aos Atendentes. O primeiro, consiste em eterminar os turnos de trabalho e a quantidade de atendentes em cada turno, de modo a satisfazer à demanda. O segundo, busca a configuração de jornadas de trabalho e a designação destas aos atendentes. Os objetivos são o de minimizar a quantidade de atendentes e de encontrar jornadas que iniciem o turno o mais próximo possível de um horário determinado. Para resolver o problema, foi desenvolvido um Algoritmo Evolutivo (AE) que integra outros AEs, denominado Algoritmo Evolutivo Adaptativo (AEA). A ideia que motivou o desenvolvimento do AEA foi a introdução de um processo que leva em consideração o desempenho prévio de cada AE. Para a resolução do primeiro problema foram utilizados Algoritmos Genéticos, Evolução Diferencial Discreta e o AEA integrando os dois algoritmos anteriores. Também, um modelo de PLI foi desenvolvido e resolvido com os aplicativos XPRESS, Cbc, Gurobi e MOSEK, disponibilizados em um site na internet. Os resultados encontrados pelos AEs se mostraram próximos aos encontrados a partir da resolução do modelo em PLI. Os resultados do AEA e do modelo em PLI foram utilizados como dados de entrada para o segundo problema. Nesta segunda fase foi desenvolvida uma EDD com variáveis mistas (inteiras e binárias). Os resultados encontrados mostraram que para se encontrar resultados adequados para o problema de Programação de Pessoal, não é necessário usar os melhores resultados encontrados na primeira etapa, mas apenas resultados adequados. O AEA desenvolvido pode integrar, além de AEs, outras ferramentas e ser utilizado em outras aplicações. A metodologia adotada pode ser considerada adequada para aplicação em empresas de Call Center, podendo ser expandida para outras com características similares.Increasing global competition has encouraged companies to make their products more competitive and more efficient services, noting the cost savings. Currently, we see a rapid growth in the services sector, what shows the importance of efficient use of available human and material resources. With the focus on this growth, particularly in the Call Center industry, this paper presents a methodology for solving Human Resource problem with an application for a company in this sector. The problem was divided into two phases, resolved in the following order: Working Shift problem and Assignment of the Shifts to the Telephone Operators problem. The first one is to determine the shifts and the number of telephone operators on each shift to meet demand. The second one seeks the setting working hours and the assignment of the telephone operators. The objectives are to minimize the number of telephone operators and find working hours that begin the shift as close as possible to a certain time. To solve the problem has been developed an Evolutionary Algorithm (EA) that integrates other EAs, called Adaptive Evolutionary Algorithm (AEA). The idea that led to the development of the AEA was the introduction of a process that takes into account the previous performance of each EA. To solve the first problem was used Genetic Algorithms, Discrete Differential Evolution and AEA integrating the two previous algorithms. Also, an ILP model was developed and solved with XPRESS, Cbc, Gurobi and MOSEK applications, available on a website. The results find to AEs showed similar to those found from solving the ILP model. The results of AEA and PLI model were used as input data for the second problem. The second phase was developed with an EDD mixed variables (integer and binary). The results showed that in order to find appropriate results for the Human Resource problem, there is no need to use the best results in the first step, but only use the adequate results. The AEA developed may include, beyond the AE, others tools to be used in other applications. The methodology can be considered suitable for application in Call Center companies and can be expanded to others with similar characteristics

Towards a more efficient use of computational budget in large-scale black-box optimization

Evolutionary algorithms are general purpose optimizers that have been shown effective in solving a variety of challenging optimization problems. In contrast to mathematical programming models, evolutionary algorithms do not require derivative information and are still effective when the algebraic formula of the given problem is unavailable. Nevertheless, the rapid advances in science and technology have witnessed the emergence of more complex optimization problems than ever, which pose significant challenges to traditional optimization methods. The dimensionality of the search space of an optimization problem when the available computational budget is limited is one of the main contributors to its difficulty and complexity. This so-called curse of dimensionality can significantly affect the efficiency and effectiveness of optimization methods including evolutionary algorithms. This research aims to study two topics related to a more efficient use of computational budget in evolutionary algorithms when solving large-scale black-box optimization problems. More specifically, we study the role of population initializers in saving the computational resource, and computational budget allocation in cooperative coevolutionary algorithms. Consequently, this dissertation consists of two major parts, each of which relates to one of these research directions. In the first part, we review several population initialization techniques that have been used in evolutionary algorithms. Then, we categorize them from different perspectives. The contribution of each category to improving evolutionary algorithms in solving large-scale problems is measured. We also study the mutual effect of population size and initialization technique on the performance of evolutionary techniques when dealing with large-scale problems. Finally, assuming uniformity of initial population as a key contributor in saving a significant part of the computational budget, we investigate whether achieving a high-level of uniformity in high-dimensional spaces is feasible given the practical restriction in computational resources. In the second part of the thesis, we study the large-scale imbalanced problems. In many real world applications, a large problem may consist of subproblems with different degrees of difficulty and importance. In addition, the solution to each subproblem may contribute differently to the overall objective value of the final solution. When the computational budget is restricted, which is the case in many practical problems, investing the same portion of resources in optimizing each of these imbalanced subproblems is not the most efficient strategy. Therefore, we examine several ways to learn the contribution of each subproblem, and then, dynamically allocate the limited computational resources in solving each of them according to its contribution to the overall objective value of the final solution. To demonstrate the effectiveness of the proposed framework, we design a new set of 40 large-scale imbalanced problems and study the performance of some possible instances of the framework