Topic
Crossover
About: Crossover is a research topic. Over the lifetime, 15599 publications have been published within this topic receiving 283676 citations.
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TL;DR: In this article , an improved crossover operator adaptive algorithm and penalty function are proposed to improve the traditional genetic algorithm, which can effectively solve the problem of local optimal solution caused by too early convergence of the traditional GA in pipe network optimization design.
Abstract: In view of the shortcomings of water supply network optimization design based on the traditional genetic algorithm in water supply safety and economy, an improved crossover operator adaptive algorithm and penalty function are proposed to improve the traditional genetic algorithm, which can effectively solve the problem of local optimal solution caused by too early convergence of the traditional genetic algorithm in pipe network optimization design. Taking a typical annular water supply network as an example, the calculation results show that the economy of the design scheme of the improved genetic algorithm is better than the traditional genetic algorithm, which fully shows that the improved genetic algorithm is practical and effective for the optimal design of water supply network.
3 citations
TL;DR: A novel multi-objective version of the Marine Predator Algorithm was introduced and was applied to several real-world software-defined networks and was compared with some state-of-the-art algorithms regarding L C−S, L C −C, Imbalance, SP, and obtained Pareto members, demonstrating the superiority of the proposed controller placement algorithm.
3 citations
TL;DR: In this article , an Energy-Efficient Distributed Assembly Blocking FlowShoP (EEDABFSP) is considered and an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is developed to solve it.
Abstract: In this study, an Energy-Efficient Distributed Assembly Blocking FlowShoP (EEDABFSP) is considered. An improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is developed to solve it. Two objectives have been considered, i.e. minimizing the maximum completion time and total energy consumption. To begin, each feasible solution is encoded as a one-dimensional vector with the factory assignment, operation scheduling and speed setting assigned. Next, two initialization schemes are presented to improve both quality and diversity, which are based on distributed assembly attributes and the slowest allowable speed criterion, respectively. Then, to accelerate the convergence process, a novel Pareto-based crossover operator is designed. Because the populations have different initialization strategies, four different mutation operators are designed. In addition, a distributed local search is integrated to improve exploitation abilities. Finally, the experimental results demonstrate that the proposed algorithm is more efficient and effective for solving the EEDABFSP.
3 citations
TL;DR: Cheng et al. as discussed by the authors used chaotic maps methods to replace the original random initialization to improve the diversity of the algorithm, which is more suitable for exploring the potential areas in the early stage.
Abstract: Slime Mould Algorithm (SMA) is a new meta-heuristics algorithm that is inspired by the behaviors of slime mould from nature. Due to its effective performance, SMA has shown its competitive performance among other meta-heuristics algorithms and has been used in many mathematical optimization and real-world problems. However, SMA tends to sink into local optimality and lacks the diversity of the population. Therefore, to cope with the drawbacks of the classical SMA, this paper proposes an improved SMA algorithm named CHDESMA. First of all, the chaotic maps methods have the characteristics of ergodicity and randomness, and we used chaotic maps methods to replace the original random initialization to improve the diversity of the algorithm, which is more suitable for exploring the potential areas in the early stage. Then, based on the superior searching ability of the differential evolution algorithm (DE), the crossover and selection operations of DE are applied to CHDESMA, and the position is updated by the combination of the original SMA operator and the mutation strategy of DE, which effectively avoids the algorithm falling into local optimum. CHDESMA was evaluated using CEC2014 and CEC2017 test suits and four real-world engineering problems. CHDESMA was compared with advanced algorithms and DE variants. The experimental results and statistical analysis indicate that CHDESMA has competitive performance compared with the state-of-the-art algorithms.
3 citations
Patent•
07 Nov 2016
TL;DR: In this paper, a system to predict vehicular traffic based on genetic programming is presented, which consists of a genetic programming database to store genetic programming parameters and training data; an evolution procedure processing unit to perform evolution procedures including crossover and mutation with respect to individuals of the current generation including functions and terminals to perform genetic programming and generate individuals of individuals from the next generation.
Abstract: A system to predict a vehicular traffic based on genetic programming according to embodiments of the present invention comprises: a genetic programming database to store genetic programming parameters and training data; an evolution procedure processing unit to perform evolution procedures including crossover and mutation with respect to individuals of the current generation including functions and terminals to perform genetic programming and generate individuals of the next generation; and an individual selection unit to select at least some individuals of the individuals of the next generation by using a fitness function giving different weighting factors to error intervals with respect to an error which is a difference between a predicted value obtained from the individuals of the next generation and training data and provide the selected individuals to the evolution procedure processing unit.
3 citations