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An improved constraint handling technique based on a comparison mechanism is presented, and then it is combined with selection operator in differential evolution to fulfill constraint handling and selection simultaneously. A differential evolution with two mutation strategies based on this new constraint handling technique is developed to solve the linear bilevel programming problems. The simulation...
Differential evolution (DE) is one of the evolutionally algorithms for solving optimization problems in a continuous space. DE has been widely applied to solve various optimization problems. Additionally, many modified DE algorithms have been developed in an attempt to improve search performance. In this paper, we propose island-based DE with varying subpopulation size. Island model is one of the...
A new adaptive Differential Evolution algorithm called EWMA-DE is proposed. In original Differential Evolution algorithm three different control parameter values must be pre-specified by the user a priori; Population size, crossover constant and mutation scale factor. Choosing good parameters can be very difficult for the user, especially for the practitioners. In the proposed algorithm the mutation...
During the search process of differential evolution (DE), each new solution may represent a new more promising region of the search space (exploration) or a better solution within the current region (exploitation). This concurrent exploitation can interfere with exploration since the identification of a new more promising region depends on finding a (random) solution in that region which is better...
Differential Evolution is a stochastic, population-based optimization algorithm that has gained wide popularity these days for solving multi-modal, non-smooth, non-convex, and ill-behaved optimization problems. In this research article, we propose a restrictive mutation strategy that helps to probabilistically select individuals for mutation based on the information conveyed by neighboring individuals...
Timetabling problem is a kind of combinatorial optimizations. However, it is very difficult to be solved the timetabling problem from the enormous combination total number and the complexity of the limitation condition. In this study, we develop the timetabling algorithm for Osaka International University (OIU). Otherwise, it is difficult to design and solving the problem with many constraints. So,...
The University course timetabling problem is known as a NP-hard problem. It is a complex problem wherein the problem size can become huge due to limited resources (e.g. amount of rooms, their capacities and number availability of lecturers) and the requirements for these resources. The university course timetabling problem involves assigning a given number of events to a limited number of timeslots...
To solve the premature convergence problem of the conventional differential evolution, an improved differential evolution algorithm is proposed in this paper. The proposed algorithm introduces mixed distribution mutation operation which combines gaussian distribution mutation and cauchy distribution mutation by a proportion for maintaining the balance of the exploration and exploitation. Experimental...
Differential evolution (DE) algorithm is a promising global optimization approach, but its control parameters are sensitive to some difficult problems, and they must be adjusted artificially for different problems some times, which is really a time consuming work. In this paper, we present a new version of DE with self-adaptive control parameters. We call the new version efficient improved differential...
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