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Abstract Differential Evolution (DE) variants have been proven to be excellent algorithms in tackling real-parameter single objective numerical optimization because they have secured the front ranks of these competitions for many years. Nevertheless, there are still some weaknesses, e.g. (1) improper control parameter adaptation schemes; and (2) defect in a given mutation strategy., existing in some state-of-the-art DE variants, which may result in slow convergence and worse optimization performance. Therefore, in this paper, a novel Parameter adaptive DE (PaDE) is proposed to tackle the above mentioned weaknesses and the PaDE algorithm has three advantages: (1) A grouping strategy with novel adaptation scheme for C r is proposed to tackle the improper adaptation schemes of C r in some state-of-the-art DE variants; (2) A novel parabolic population size reduction scheme is proposed to tackle the weakness in linear population size reduction scheme; (3) An enhanced time stamp based mutation strategy is proposed to tackle the weakness in a former mutation strategy. The novel PaDE algorithm is verified under 58 benchmarks from two Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single objective numerical optimization, and experiment results show that the proposed PaDE algorithm is competitive with the other state-of-the-art DE variants. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a simple but powerful population-based stochastic optimization algorithm. Owing to its simplicity, easy implementation and excellent performance, DE has been wildly applied in scientific and engineering areas. However, there are still some inconveniences and weaknesses in DE algorithm, such as the inconveniences in the choice of proper control parameters and the defects existing in a given mutation strategy. In this paper, a new DE variant, called Parameters with Adaptive Learning Mechanism Differential Evolution (PALM-DE), is proposed to tackle the inconvenience in control parameter selection as well as to enhance a former mutation strategy. The new variant is verified on 44 commonly used real-parameter single objective benchmark functions selected from CEC2013 and CEC2014 competitions. Several recently proposed well-known DE variants are also contrasted in the paper, and the experiment results show that the proposed PALM-DE algorithm is competitive in comparison with these DE variants. An attempt to enhance the performance of PALM-DE by employing linear population size reduction is also presented, and the performance is still competitive. [ABSTRACT FROM AUTHOR]

Optimization algorithms are proposed to tackle different complex problems in different areas. In this paper, we firstly put forward a new memetic evolutionary algorithm, named Monkey King Evolutionary (MKE) Algorithm, for global optimization. Then we make a deep analysis of three update schemes for the proposed algorithm. Finally we give an application of this algorithm to solve least gasoline consumption optimization (find the least gasoline consumption path) for vehicle navigation. Although there are many simple and applicable optimization algorithms, such as particle swarm optimization variants (including the canonical PSO, Inertia Weighted PSO, Constriction Coefficients PSO, Fully Informed Particle Swarm, Comprehensive Learning Particle Swarm Optimization, Dynamic Neighborhood Learning Particle Swarm). These algorithms are less powerful than the proposed algorithm in this paper. 28 benchmark functions from BBOB2009 and CEC2013 are used for the validation of robustness and accuracy. Comparison results show that our algorithm outperforms particle swarm optimizer variants not only on robustness and optimization accuracy, but also on convergence speed. Benchmark functions of CEC2008 for large scale optimization are also used to test the large scale optimization characteristic of the proposed algorithm, and it also outperforms others. Finally, we use this algorithm to find the least gasoline consumption path in vehicle navigation, and conducted experiments show that the proposed algorithm outperforms A* algorithm and Dijkstra algorithm as well. [ABSTRACT FROM AUTHOR]

Optimization problems exists extensively in real life, especially in science and engineering. Over the past decades, various optimization techniques have been developed to solve complex optimization problems in different areas especially that are unable to be solved by traditional methods. QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm is a new novel evolution structure for global optimization, which is a swarm based algorithm and use quasi-affine transformation approach for evolution. Nevertheless, there are still some weaknesses in these QUATRE variants. This paper presents a novel E-QUATRE algorithm in which an automatically generated evolution matrix with self-adaptive mechanism and an adaptive control parameter F are proposed for the enhancement of the QUATRE algorithm. Moreover, this paper also discusses the relationship between QUATRE algorithm, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm, all of which are also famous swarm based Stochastic Algorithms (SAs). Algorithm validation is conducted under CEC2013 test suite on single-objective numerical optimization, and E-QUATRE algorithm is compared with several famous Particle Swarm Optimization (PSO) variants, Differential Evolution (DE) variants and QUATRE variants. The experiment results indicate that the proposed E-QUATRE algorithm has a better performance than these swarm based algorithms with fixed population. [ABSTRACT FROM AUTHOR]