Multiobjective evolutionary computation algorithms for solving task scheduling problem on heterogeneous systems
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The task scheduling problem in heterogeneous systems (TSPHS) is a NP-complete problem. It is a multiobjective optimization problem (MOP). The objectives such as makespan, average flow time, robustness and reliability of the schedule are conWhile using evolutionary strategy to solve multiobjective optimization,in order to improve exploration of the solutions in decision space and maintain the diversity of the pareto front,a multiobjective optimization algorithm based on evolutionary strategy is presented.The evolutionary strategy of self-adaptive mutation step is used to search solutions in the globle area and local area.And the non-dominated solution in certain ratio enters the next generation,so the dominated individual has opportunity to participate multiplying in the next generation,and the diversity of the pareto front is assured.The simulation results show the good performance of the proposed algorithm.
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It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto front in large-scale multiobjective optimization. To tackle this problem, this article proposes a large-scale multiobjective evolutionary algorithm assisted by some selected individuals generated by directed sampling (DS). At each generation, a set of individuals closer to the ideal point is chosen for performing a DS in the decision space, and those nondominated ones of the sampled solutions are used to assist the reproduction to improve the convergence in evolutionary large-scale multiobjective optimization. In addition, elitist nondominated sorting is adopted complementarily for environmental selection with a reference vector-based method in order to maintain diversity of the population. Our experimental results show that the proposed algorithm is highly competitive on large-scale multiobjective optimization test problems with up to 5000 decision variables compared to five state-of-the-art multiobjective evolutionary algorithms.
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Association rule analysis has been widely employed as a basic technique for data mining. Extensive research has also been conducted to apply evolutionary computing techniques to the field of data mining. This study presents a method to evaluate the settings of evolutionary operations in evolutionary rule discovery method, which is characterized by the execution of overall problem solving through the acquisition and accumulation of small results. Since the purpose of population evolution is different from that of general evolutionary computation methods that aim at discovering elite individuals, we examined the difference in the concept of settings during evolution and the evaluation of evolutionary computation by visualizing the progress and efficiency of problem solving. The rule discovery method (GNMiner) is characterized by reflecting acquired information in evolutionary operations; this study determines the relationship between the settings of evolutionary operations and the progress of each task execution stage and the achievement of the final result. This study obtains knowledge on the means of setting up evolutionary operations for efficient rule-set discovery by introducing an index to visualize the efficiency of outcome accumulation. This implies the possibility of setting up dynamic evolutionary operations in the outcome accumulation-type evolutionary computation in future studies.
Human-based evolutionary computation
Evolutionary music
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Multiobjective optimization aims to simultaneously optimize two or more objectives for a problem, with multiobjective evolutionary algorithms (MOEAs) having become a popular research topic in evolutionary multiobjective optimization. We first define the multiobjective optimization problem and briefly summarize multiobjective optimization methods based on the evolutionary algorithm. Representative MOEAs from three categories are then introduced in detail, and we discuss some of the problems and challenges in improving MOEAs. Finally, future research directions for MOEAs are proposed.
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Some interesting features of the new book "Introduction to Evolutionary Algorithms", which is written by Xinjie Yu and Mitsuo Gen and be published by Springer in 2010, will be illustrated, including covering nearly all the hot evolutionary-computation-related topics, referring to the latest published journal papers, introducing the applications of EAs as many as possible, and adopting many pedagogical ways to make EAs easy and interesting. The contents and the consideration of selecting these contents will be discussed. Then the focus will be put on the pedagogical ways for teaching and self-study. The algorithms introduced in the tutorial include constrained optimization evolutionary algorithms (COEA), multiobjective evolutionary algorithms (MOEA), ant colony optimization (ACO), particle swarm optimization (PSO), and artificial immune systems (AIS). Some of the applications of these evolutionary algorithms will be discussed.
Human-based evolutionary computation
Parallel metaheuristic
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In evolutionary multiobjective optimization, the Pareto front (PF) is approximated by using a set of representative candidate solutions with good convergence and diversity. However, most existing multiobjective evolutionary algorithms (MOEAs) have general difficulty in the approximation of PFs with complicated geometries. To address this issue, we propose a generic front modeling method for evolutionary multiobjective optimization, where the shape of the nondominated front is estimated by training a generalized simplex model. On the basis of the estimated front, we further develop an MOEA, where both the mating selection and environmental selection are driven by the approximate nondominated fronts modeled during the optimization process. For performance assessment, the proposed algorithm is compared with several state-of-the-art evolutionary algorithms on a wide range of benchmark problems with various types of PFs and different numbers of objectives. Experimental results demonstrate that the proposed algorithm performs consistently on a variety of multiobjective optimization problems.
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Simplex
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During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.
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A comparative study of newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) applied to a nonlinear power system multiobjective optimization problem is presented in this paper. Specifically, Niched Pareto genetic algorithm (NPGA), nondominated sorting genetic algorithm (NSGA), and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to environmental/economic electric power dispatch (EED) problem. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus test system. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. The results of MOEA have been compared to those reported in the literature. The comparison shows the superiority of MOEA to the traditional multiobjective optimization techniques and confirms their potential to handle power system multiobjective optimization problems.
Economic Dispatch
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