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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 algorithm's 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(More)
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(More)
— 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(More)
The direction of improvement has been discussed and used to guide MOEAs during the search process towards the area of Pareto optimal set. One of typical examples using direction of improvement is the Direction based Multi-objective Evolutionary Algorithm (DMEA). For DMEA, its authors introduced a novel algorithm incorporating the concept of direction of(More)
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(More)
In this chapter, we discuss the use of multiobjective evolutionary algorithms (MOEAs) for solving single-objective optimization problems in dynamic environments. Specifically, we investigate the consideration of a second (artificial) objective, with the aim of maintaining greater population diversity and adaptability. The paper suggests and compares a(More)
— In this paper, we present a novel efficient strategy to improve the performance of the differential evolution (DE) algorithm for real parameter optimization, by generating a variable step length based on a probability distribution, instead of using the conventional fixed step length approach. Previous studies investigated uniform and Gaussian(More)