A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems

2017 2nd International Conference on Mechatronics and Information Technology (ICMIT 2017)

2017

Aiming at the defects of differential evolution, such as premature convergence, low accuracy and other shortcomings, an improved differential evolution algorithm with novel mutation strategy(NMSIDE) is presented. The novel mutation strategy is used to avoid being trapped into local minima for NMSIDE. If the evolution of the individual stagnates, the individual will rely on the current best individual to move closer to the global best individual. The mutation rate varies dynamically within the range of values, and the crossover rate is dynamically varied based on the number of iterations. NMSIDE is tested on 11 standard functions and compared with the other state-of-the-art algorithms. The experimental results show that NMSIDE has higher convergence precision, faster convergence speed and better robustness.

“…NMSIDE is compared with the recent well-known approaches, such as jdDE [18], RMDE [19], HSDE [8] and the basic DE algorithm. The parameters of the algorithm are set as follows.…”

Section: Comparison Of Algorithm Resultsmentioning

confidence: 99%

mentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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An Improved Differential Evolution Algorithm with Novel Mutation Strategy

Shen¹,

Zou²,

2017 2nd International Conference on Mechatronics and Information Technology (ICMIT 2017)

2017

“…Now, the key task is how to select a generation index threshold between 1 and to divide the entire evolution process into the early and late phase. Many scholars tend to set a probability rule based on the number of iterations [34,35] or the fitness value of random individual [36] to determine whether the current evolution lies in the early phase or late phase of iteration. Different from their methods, Tang and Dong et al [37] introduced the success ratio and an auxiliary generation index threshold to help select the generation index threshold .…”

Section: A Phase-based Mutation Operation In De Mutationmentioning

confidence: 99%

A Phase-Based Adaptive Differential Evolution Algorithm for the Economic Load Dispatch Considering Valve-Point Effects and Transmission Losses

Shen

Zou

Mathematical Problems in Engineering

et al. 2018

A phase-based adaptive differential evolution (PADE) algorithm is proposed to solve the economic load dispatch (ELD) considering valve-point effects (VPE) and transmission losses. To a great extent, PADE makes up for the drawbacks of the traditional differential evolution (DE) through three improvements. First, we establish an archive of storing successful individuals to improve the quality of offspring. Second, to balance the exploring and exploiting ability of the algorithm, a phase-based mutation operation is carried out. Third, two control parameters are adaptively adjusted, which is helpful for enhancing the robustness of the algorithm. In addition, two types of repair methods of constraint handling are employed for the ELD without or with transmission losses to help PADE find feasible solutions more efficiently. A performance comparison between PADE and other DE approaches from the literature was carried out on six ELD test cases which consider a set of operating constraints including the VPE and transmission losses. Results show a competitive PADE performance in all test cases regarding the compared DE approaches. Compared to methods from the literature, the costs obtained by PADE are lower in most cases while the corresponding constraint violations reach a lower level.

“…In [32], several operators are all calculated and the results with good fitness are selected. In the research of differential evolution algorithms, a variety of mutation operators are proposed, and the research on adaptive ensemble of multiple mutation operators has attracted the interest of researchers [33][34][35][36][37]. Some studies have further explored the combination of multiple crossover operators and multiple mutation operators in differential evolutionary algorithms [38].…”

Section: Introductionmentioning

confidence: 99%

A General Intelligent Optimization Algorithm Combination Framework with Application in Economic Load Dispatch Problems

Dong

2019

Energies

Recently, a population-based intelligent optimization algorithm research has been combined with multiple algorithms or algorithm components in order to improve the performance and robustness of an optimization algorithm. This paper introduces the idea into real world application. Different from traditional algorithm research, this paper implements this idea as a general framework. The combination of multiple algorithms or algorithm components is regarded as a complex multi-behavior population, and a unified multi-behavior combination model is proposed. A general agent-based algorithm framework is designed to support the model, and various multi-behavior combination algorithms can be customized under the framework. Then, the paper customizes a multi-behavior combination algorithm and applies the algorithm to solve the economic load dispatch problems. The algorithm has been tested with four test systems. The test results prove that the multi-behavior combination idea is meaningful which also indicates the significance of the framework. Energies 2019, 12, 2175 2 of 22 2.3.2. Competitive Strategy The competition strategy means that multiple behaviors compete with each other and decide the winner. The winner would acquire more resources as the reward. The competitive resource of the individual level combination is the number of individuals, and the resource of the iteration Energies 2019, 12, 2175 6 of 22