Dynamic differential evolution with oppositional orthogonal crossover for large scale optimisation problems

This paper proposes a novel distributed differential evolution framework called distributed mixed variants (dynamic) differential evolution ( $$dmvD^{2}E)$$ d m v D 2 E ) . This novel framework is a heterogeneous mix of effective differential evolution (DE) and dynamic differential evolution (DDE) variants with diverse characteristics in a distributed framework to result in $$dmvD^{2}E$$ d m v D 2 E . The $$dmvD^{2}E$$ d m v D 2 E , discussed in this paper, constitute various proportions and combinations of DE/best/2/bin and DDE/best/2/bin as subpopulations with each variant evolving independently but also exchanging information amongst others to co-operatively enhance the efficacy of $$dmvD^{2}E$$ d m v D 2 E as whole. The $$dmvD^{2}E$$ d m v D 2 E variants have been run on 14 test problems of 30 dimensions to display their competitive performance over the distributed classical and dynamic versions of the constituent variants. The $$dmvD^{2}E$$ d m v D 2 E , when benchmarked on a different 13 test problems of 500 as well as 1,000 dimensions, scaled well and outperformed, on an average, five existing distributed differential evolution algorithms.

The power system reactive power optimization model mainly establishes a model to reduce the line active power loss as an indicator. The model is established with the least active power loss, the highest voltage stability margin and the smallest voltage deviation. Aiming at the shortcomings of differential evolution algorithm which is easy to fall into local optimum, this paper proposes to apply dynamic multi-subgroup differential evolution algorithm to power system reactive power optimization. The dynamic subgroup partitioning feature of the algorithm improves the global search ability, and increases the polymorphism of species during the iterative process. The dynamic multi-subgroup differential evolution algorithm and differential evolution algorithm are substituted into the power system IEEE-14 nodes for simulation comparison, and the feasibility and superiority of the algorithm are verified. Comparing the simulation diagrams of the two algorithms, it can be seen that the dynamic multi-subgroup differential evolution algorithm has significant advantages in power system reactive power optimization.

The application of two techniques for the reconstruction of shape reconstruction of a metallic cylinder from scattered field measurements is studied in this paper. These techniques are applied to two-dimensional configurations, for which the method of moment (MoM) is applied to solve the integral equations. Considering that the microwave imaging is recast as a nonlinear optimization problem, an objective function is defined by the norm of the difference between the measured scattered electric fields and those calculated for each estimated metallic cylinder. Thus, the shape of a metallic cylinder can be obtained by minimizing the objective function. In order to solve this inverse scattering problem, two techniques are employed. The first one is based on dynamic differential evolution (DDE) algorithm, while the second one is an improved version of the DDE algorithm with self-adaptive control parameters, called SADDE. Both techniques are tested for the simulated data contaminated by additive white Gaussian noise. Numerical results indicate that SADDE algorithm outperforms DDE algorithm in terms of reconstruction accuracy and convergence speed.

t—Differential evolution algorithm in solving complex function optimization problems, the problems of convergence rate and precision is not high. At the same time, there is a big difference in the performance of evolutionary algorithms for solving the different types of optimization problems. To solve above two problems, this paper proposes a dynamic multimodal differential evolution algorithm. Firstly, the dynamically population is used to improve the exploration ability of algorithms; In addition, the algorithm uses Four different types of mutation operator to Produce among individuals, choose the best among individuals to enter the next iteration , improved the algorithms's performance of solving different types of optimization problems. Through a variety of BenchMark functions to the algorithm simulation experiment, and comparing and several other classical differential evolution algorithm, show that this algorithm has better optimization performance. Keywords—Differential evolution, Convergence rate, Mutation Operator 一种动态多模式差分演化算法 董小刚 ,谢清 ,柯林 2 1) 九江学院信息科学与技术学院,九江 江西 中国

An empirical study of the algorithm dynamic differential evolution with combined variants with a repair method (DDECV $$+$$+ Repair) in the solution of dynamic constrained optimization problems is presented. Unexplored aspects of the algorithm are of particular interest in this work: (1) the role of each one of its elements, (2) its sensitivity to different change frequencies and change severities in the objective function and the constraints, (3) its ability to detect a change and recover after it, besides its diversity handling (percentage of feasible and infeasible solutions) during the search, and (4) its performance with dynamism present in different parts of the problem. Seven performance measures, eighteen recently proposed test problems and eight algorithms found in the specialized literature are considered in four experiments. The statistically validated results indicate that DDECV $$+$$+ Repair is robust to change frequency and severity variations, and that it is particularly fast to recover after a change in the environment, but highly depends on its repair method and its memory population to provide competitive results. DDECV $$+$$+ Repair shows evidence on the convenience of keeping a proportion of infeasible solutions in the population when solving dynamic constrained optimization problems. Finally, DDECV $$+$$+ Repair is highly competitive particularly when dynamism is present in both, objective function and constraints.

The coordination of population structure is the foundation for the effective functioning of evolutionary algorithms. An efficient population evolution structure can guide individuals to engage in successful and robust exploitative and exploratory behaviors. However, due to the black-box property of the search process, it is challenging to assess the current state of the population and implement targeted measures. In this paper, we propose a dynamic population structures-based differential evolution algorithm (DPSDE) to uncover the real-time state of population continuous optimization. According to the exploitation and exploration state of population, we introduce four structural modules to address the premature convergence and search stagnation issues of the current population. To effectively utilize these modules, we propose a real-time discernment mechanism to judge the population's current state. Based on the feedback information, suitable structural modules are dynamically invoked, ensuring that the population undergoes continuous and beneficial evolution, ultimately exploring the optimal population structure. The comparative outcomes with numerous cutting-edge algorithms on the IEEE Congress on Evolutionary Computation (CEC) 2017 benchmark functions and 2011 real-world problems verify the superiority of DPSDE. Furthermore, parameters, population state, and ablation study of modules are discussed.

A dynamic shuffled differential evolution algorithm for data clustering

In this paper, a population-based direct global optimization technique called Dynamic Population Size Differential Evolution (DynNP-DE) to deal with partial shading occurrence is proposed. In this proposed technique, the size of the population is significantly reduced without compromising the robustness of the original DE algorithm. To evaluate the effectiveness of the proposed MPPT technique, the algorithm is implemented on a boost converter and validated through several partial-shading patterns. Simulation results show that the proposed modification yields faster convergence and capable to handle partial shading conditions.

A novel strategy for synthesizing shaped power pattern by using element rotation and phase optimization for a linear dipole array is presented. Element rotation can be considered as an array amplitude weighting technique, and it provides additional degrees of freedom in the pattern synthesis, but potentially increases the cross-polarization level (XPL). In order to obtain a desired copolarized shaped pattern with both the sidelobe level and XPL constrained, we simultaneously optimize all the rotation angles and excitation phases of all the dipole elements by applying the dynamic differential evolution algorithm. Two experiments for synthesizing flat-top and cosecant-squared patterns are conducted to validate the effectiveness and advantages of the proposed strategy.

This paper investigates optimization in dynamic environments where the numbers of optima are unknown or fluctuating. The authors present a novel algorithm, Dynamic Population Differential Evolution (DynPopDE), which is specifically designed for these problems. DynPopDE is a Differential Evolution based multi-population algorithm that dynamically spawns and removes populations as required. The new algorithm is evaluated on an extension of the Moving Peaks Benchmark. Comparisons with other state-of-the-art algorithms indicate that DynPopDE is an effective approach to use when the number of optima in a dynamic problem space is unknown or changing over time.

An improved dynamic multi-objective optimization approach for nonlinear equation systems

A dynamic-speciation-based differential evolution with ring topology for constrained multimodal multi-objective optimization

Butterfly optimisation algorithm (BOA) is a newcomer in the family of nature inspired optimisation algorithms. Although it is an effective algorithm, still, like other population-based optimisation algorithms, it encounters two probable problems: 1) entrapment in local optima; 2) slow convergence speed. In order to increase the potential of the algorithm, it is hybridised with an efficient algorithm, differential evolution (DE), which accelerates the global convergence speed to the true global optimum while preserving the main feature of the basic BOA. In this paper, a novel hybrid algorithm based on BOA and DE, namely BOA/DE is proposed to solve numerical optimisation problems. The proposed algorithm has advantages of both BOA and DE which enable the algorithm to balance the tradeoff between exploration and exploitation which produces efficient results. Engineering design problem and standard benchmark functions are employed to validate the proposed algorithm and according to the simulation results, the performance of the hybrid algorithm is superior to or at least highly competitive with the standard BOA and DE.

An implementation of differential evolution algorithm for inversion of geoelectrical data

Optimum FIR Filter Design Using Dynamic Differential Evolution with Hybrid Mutation Strategy