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Aiming to address the defects of the arithmetic optimization algorithm (AOA), such as easy fall into local optimums and slow convergence speed during the search process, an improved arithmetic optimization algorithm (IAOA) is proposed and applied to the study of distribution network reconfiguration. Firstly, a reconfiguration model is established to reduce network loss, and a cosine control factor is introduced to reconfigure the math optimization accelerated (MOA) function to coordinate the algorithm's global exploration and local exploitation capabilities. Subsequently, a reverse differential evolution strategy is introduced to improve the overall diversity of the population and Weibull mutation is performed on the better-adapted individuals generated in each iteration to ensure the quality of the optimal individuals generated in each iteration and strengthen the algorithm's ability to approach the optimal solution. The performance of the improved algorithm is also tested using eight basis functions. Finally, simulation analysis is carried out by taking the IEEE33 and IEEE69 node systems and a real power distribution system as examples; the results show that the proposed algorithm can help to reconfigure the system quickly, and the system node voltages and network losses were significantly improved after the reconfiguration. [ABSTRACT FROM AUTHOR]

This article deals with a simultaneous abstract evolution equation. This includes a parabolic-hyperbolic phase-field system as an example which consists of a parabolic equation for the relative temperature coupled with a semilinear damped wave equation for the order parameter (see e.g., Grasselli and Pata [Adv. Math. Sci. Appl.13 (2003) 443–459, Comm. Pure Appl. Anal.3 (2004) 849–881], Grasselli et al. [Comm. Pure Appl. Anal.5 (2006) 827–838], Wu et al. [Math. Models Methods Appl. Sci.17 (2007) 125–153, J. Math. Anal. Appl.329 (2007) 948–976]). On the other hand, a time discretization of an initial value problem for an abstract evolution equation has been studied (see e.g., Colli and Favini [Int. J. Math. Math. Sci.19 (1996) 481–494]) and Schimperna [J. Differ. Equ.164 (2000) 395–430] has established existence of solutions to an abstract problem applying to a nonlinear phase-field system of Caginalp type on a bounded domain by employing a time discretization scheme. In this paper we focus on a time discretization of a simultaneous abstract evolution equation applying to parabolic-hyperbolic phase-field systems. Moreover, we can establish an error estimate for the difference between continuous and discrete solutions. [ABSTRACT FROM AUTHOR]

Exploration and exploitation are contradictory in differential evolution (DE) algorithm. In order to balance the search behavior between exploitation and exploration better, a novel self-adaptive dual-strategy differential evolution algorithm (SaDSDE) is proposed. Firstly, a dual-strategy mutation operator is presented based on the “DE/best/2” mutation operator with better global exploration ability and “DE/rand/2” mutation operator with stronger local exploitation ability. Secondly, the scaling factor self-adaption strategy is proposed in an individual-dependent and fitness-dependent way without extra parameters. Thirdly, the exploration ability control factor is introduced to adjust the global exploration ability dynamically in the evolution process. In order to verify and analyze the performance of SaDSDE, we compare SaDSDE with 7 state-of-art DE variants and 3 non-DE based algorithms by using 30 Benchmark test functions of 30-dimensions and 100-dimensions, respectively. The experiments results demonstrate that SaDSDE could improve global optimization performance remarkably. Moreover, the performance superiority of SaDSDE becomes more significant with the increase of the problems’ dimension. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is powerful for global optimization problems. Among DE algorithms, JADE and its variants, whose mutation strategy is DE/current-to-pbest/1 with optional archive, have good performance. A significant feature of the above mutation strategy is that one individual for difference operation comes from the union of the optional external archive and the population. In existing DE algorithms based on the mutation strategy—JADE and its variants, individuals eliminated from the population are send to the archive. In this paper, we propose a scheme for managing the optional external archive. According to our scheme, two subpopulations are maintained in the population. Each of them regards the other as the archive. In experiments, our scheme is applied in JADE and two of its variants—SHADE and L-SHADE. Experimental results show that our scheme can enhance JADE and its variants. Moreover, it can be seen that L-SHADE with our scheme performs significantly better than four DE algorithms, CoBiDE, MPEDE, EDEV, and MLCCDE. [ABSTRACT FROM AUTHOR]

In any sport the selection of players for a team is fundamental for its subsequent performance. Many factors condition the selection process from the characteristics of the sport discipline to financial limitations, including a long list of restrictions associated with the environment of the competitions in which the team takes part. All of this makes the process of selecting a roster of players very complex, as it is affected by multiple variables and in many cases marked by a great deal of subjectivity. The purpose of this article was to objectively select the players for a basketball team using an evolutionary algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) that uses stochastic search methods based on the imitation of natural biological evolution. The sample was composed of the players from the teams competing in the top Spanish basketball league, the Association of Basketball Clubs (ACB). To assess the quality of the solutions obtained, the results were compared with the teams in the ACB playing in the same competition as the players used in the study. The results make it possible to obtain different solutions for composing teams rendering financial resources profitable and taking into account the restrictions of the competition and of each sport management. [ABSTRACT FROM AUTHOR]

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm. [ABSTRACT FROM AUTHOR]

The current research study is concerned with the automated differentiation between histopathological slides from colon tissues with respect to four classes (healthy tissue and cancerous of grades 1, 2 or 3) through an optimized ensemble of predictors. Six distinct classifiers with prediction accuracies ranging from 87% to 95% are considered for the task. The proposed method of combining them takes into account the probabilities of the individual classifiers for each sample to be assigned to any of the four classes, optimizes weights for each technique by differential evolution and attains an accuracy that is significantly better than the individual results. Moreover, a degree of confidence is defined that would allow the pathologists to separate the data into two distinct sets, one that is correctly classified with a high level of confidence and the rest that would need their further attention. The tandem is also validated on other benchmark data sets. The proposed methodology proves to be efficient in improving the classification accuracy of each algorithm taken separately and performs reasonably well on other data sets, even with default weights. In addition, by establishing a degree of confidence the method becomes more viable for use by actual practitioners. [ABSTRACT FROM AUTHOR]

3D point cloud registration is a key technology in 3D point cloud processing, such as 3D reconstruction, object detection. Trimmed Iterative Closest Point algorithm is a prevalent method for registration of two partially overlapping clouds. However, it relies heavily on the initial value and is liable to be trapped in to local optimum. In this paper, we adapt the Differential Evolution algorithm to obtain global optimal solution. By design appropriate evolutionary operations, the algorithm can make the populations distributed more widely, and keep the individuals from concentrating to a local optimum. In the experiment, the proposed algorithm is compared with existing methods which are based on global optimization algorithm such as Genetic Algorithm and particle filters. And the results have demonstrated that the proposed algorithm is more robust and can converge to a good result in fewer generations. [ABSTRACT FROM AUTHOR]

In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness. [ABSTRACT FROM AUTHOR]

Since wind power is integrated into the thermal power operation system, dynamic economic emission dispatch (DEED) has become a new challenge due to its uncertain characteristics. This paper proposes an adaptive grid based multi-objective Cauchy differential evolution (AGB-MOCDE) for solving stochastic DEED with wind power uncertainty. To properly deal with wind power uncertainty, some scenarios are generated to simulate those possible situations by dividing the uncertainty domain into different intervals, the probability of each interval can be calculated using the cumulative distribution function, and a stochastic DEED model can be formulated under different scenarios. For enhancing the optimization efficiency, Cauchy mutation operation is utilized to improve differential evolution by adjusting the population diversity during the population evolution process, and an adaptive grid is constructed for retaining diversity distribution of Pareto front. With consideration of large number of generated scenarios, the reduction mechanism is carried out to decrease the scenarios number with covariance relationships, which can greatly decrease the computational complexity. Moreover, the constraint-handling technique is also utilized to deal with the system load balance while considering transmission loss among thermal units and wind farms, all the constraint limits can be satisfied under the permitted accuracy. After the proposed method is simulated on three test systems, the obtained results reveal that in comparison with other alternatives, the proposed AGB-MOCDE can optimize the DEED problem while handling all constraint limits, and the optimal scheme of stochastic DEED can decrease the conservation of interval optimization, which can provide a more valuable optimal scheme for real-world applications. [ABSTRACT FROM AUTHOR]

Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run. [ABSTRACT FROM AUTHOR]

In order to optimize aircraft pushback management during rush hour, aircraft pushback slot allocation based on congestion pricing is explored while considering monetary compensation based on the quality of the surface operations. First, the concept of the “external cost of surface congestion” is proposed, and a quantitative study on the external cost is performed. Then, an aircraft pushback slot allocation model for minimizing the total surface cost is established. An improved discrete differential evolution algorithm is also designed. Finally, a simulation is performed on Xinzheng International Airport using the proposed model. By comparing the pushback slot control strategy based on congestion pricing with other strategies, the advantages of the proposed model and algorithm are highlighted. In addition to reducing delays and optimizing the delay distribution, the model and algorithm are better suited for use for actual aircraft pushback management during rush hour. Further, it is also observed they do not result in significant increases in the surface cost. These results confirm the effectiveness and suitability of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. [ABSTRACT FROM AUTHOR]

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. [ABSTRACT FROM AUTHOR]

Optimal reactive power planning is one of the major and important problems in electrical power systems operation and control. This is nothing but multi-objective, nonlinear, minimization problem of power system optimization. This paper presents the relevance of New Improved Differential Evolution (NIDE) algorithm to solve the Reactive Power Planning (RPP) problem based on Multi-objective optimization. Minimization of total cost of energy loss and cost of F A C T controlers installments are taken as the objectives incorporating (RPP) problem. With help of New Voltage Stability Index (NVSI), the critical lines and buses are identified to install the FACTS controllers. The optimal settings of the control variables of the generator voltages, transformer tap settings and provision and parameter settings of the FACT controllers SVC, TCSC, and UPFC are considered for reactive power planning. The approach applied to IEEE 30 and 72-bus Indian system for minimization of active power loss. Simulation results are compared with other optimization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new multi-objective uniform-diversity differential evolution (MUDE) algorithm is proposed and used for Pareto optimum design of mechanisms. The proposed algorithm uses a diversity preserving mechanism called the ε-elimination algorithm to improve the population diversity among the obtained Pareto front. The proposed algorithm is firstly tested on some constrained and unconstrained benchmarks proposed for the special session and competition on multi-objective optimizers held under IEEE CEC 2009. The inverted generational distance (IGD) measure is used to assess the performance of the algorithm. Secondly, the proposed algorithm has been used for multi-objective optimization of two different combinatorial case studies. The first case contains a two-degree of freedom leg mechanism with springs. Three conflicting objective functions that have been considered for Pareto optimization are namely, leg size, vertical actuating force, and the peak crank torque. The second case is a two-finger robot gripper mechanism with two conflicting objectives which are the difference between the maximum and minimum gripping force and the transmission ratio of actuated and experienced gripper forces. Comparisons of obtained Pareto fronts using the method of this work with those obtained in other references show significant improvements. [ABSTRACT FROM AUTHOR]

We analyze the existence of pullback attractor for non-autonomous differential inclusions with infinite delays in Banach spaces by using measures of noncompactness. The obtained results can be applied to control systems driven by semilinear partial differential equations and multivalued feedbacks. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to compare the efficiency of four different algorithmic parallelization methods for inverse shape design flow problems. Design/methodology/approach – The included algorithms are: a parallelized differential evolution algorithm; island-model differential evolution with multiple subpopulations; Nash differential evolution with geometry decomposition using competitive Nash games; and the new Global Nash Game Coalition Algorithm (GNGCA) which combines domain and geometry decomposition into a "distributed one-shot" method. The methods are compared using selected academic reconstruction problems using a different number of simultaneous processes. Findings – The results demonstrate that the geometry decomposition approach can be used to improve algorithmic convergence. Additional improvements were achieved using the novel distributed one-shot method. Originality/value – This paper is a part of series of articles involving the GNGCA method. Further tests implemented for more complex problems are needed to study the efficiency of the approaches in more realistic cases. [ABSTRACT FROM AUTHOR]

Nonlinear optimization algorithms could be divided into local exploitation methods such as Nelder-Mead (NM) algorithm and global exploration ones, such as differential evolution (DE). The former searches fast yet could be easily trapped by local optimum, whereas the latter possesses better convergence quality. This paper proposes hybrid differential evolution and NM algorithm with re-optimization, called as DE-NMR. At first a modified NM, called NMR is presented. It re-optimizes from the optimum point at the first time and thus being able to jump out of local optimum, exhibits better properties than NM. Then, NMR is combined with DE. To deal with equal constraints, adaptive penalty function method is adopted in DE-NMR, which relaxes equal constraints into unequal constrained functions with an adaptive relaxation parameter that varies with iteration. Benchmark optimization problems as well as engineering design problems are used to experiment the performance of DE-NMR, with the number of function evaluation times being employed as the main index of measuring convergence speed, and objective function values as the main index of optimum's quality. Non-parametric tests are employed in comparing results with other global optimization algorithms. Results illustrate the fast convergence speed of DE-NMR. [ABSTRACT FROM AUTHOR]

Abstract: Two modified versions of the authors’ recent differential evolution algorithm for constrained global optimization are proposed. They incorporate a filter set which results in more efficient implementations of the original algorithm. Numerical results are presented which suggest that the new algorithms are competitive. [Copyright &y& Elsevier]

Abstract: Differential evolution (DE) is a heuristic method that has yielded promising results for solving complex optimization problems. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution, and robustness. This paper utilizes a DE strategy to parameters estimation of structural systems, which could be formulated as a multi-modal numerical optimization problem with high dimension. Simulation results for identifying the parameters of structural systems under conditions including limited output data, noise polluted signals, and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]

Polygonal approximation of digitized curves is an important step in shape representation and analysis. One of the approaches to obtain a good polygonal approximation is that of formulating the approximation task as an optimization problem and then solving it using the available tools. Adopting this strategy, in this article, we present a differential evolution-based method for polygonal approximation. We show that our method not only yields near-optimal results, but also does so very efficiently. The proposed method is also a demonstration of the successful application of differential evolution for solving a combinatorial optimization problem. [ABSTRACT FROM AUTHOR]