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Owing to its large scale, the blocking flow-shop scheduling problem (BFSP) cannot be solved effectively by traditional optimization methods. To solve the problem, this paper develops a novel intelligent optimization algorithm based on differential evolution (DE) for the BFSP with a single objective: minimizing the total flow time (TFT). On the one hand, a new heuristic method was introduced to balance the quality and diversity of the initial population. On the other hand, a new operator was adopted to update the acceleration, velocity and position of each particle. In this way, the population will not converge prematurely to local optimums, and the local and global search abilities are perfectly balanced. Simulation on standard test set proves that our algorithm outperformed most commonly used methods in solving the BFSP. (Received, processed and accepted by the Chinese Representative Office.) [ABSTRACT FROM AUTHOR]

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent. [ABSTRACT FROM AUTHOR]

This paper puts forward a multi-objective hybrid difference optimization algorithm to solve multiobjective flow-shop scheduling problem (FSP). The hybrid algorithm inherits the merits of differential evolution vector operation, and makes dynamic adjustments to the search direction based on historical data. However, the basic differential evolution algorithm is prone to the local optimum trap, due to the low population diversity in the later stage of evolution. To solve the problem, a hybrid sampling strategy was introduced obtain the distribution information of solution sets and to design the mutation operator of differential evolution, thus improving the convergence of the hybrid algorithm. Finally, our algorithm was applied to solve FSPs through simulation. The simulation results show that our algorithm greatly outperformed the basic multi-objective evolutionary algorithm in convergence and distribution performance. [ABSTRACT FROM AUTHOR]

The stability of road embankment is influenced by two main factors, namely slope stability and settlement. The use of an adaptive neuro-fuzzy inference system (ANFIS) has received encouraging responses over the last decade in various research areas. This paper aims to elaborate on the previous study on the application of ANFIS to predict factors that affect the stability of road embankment. Additionally, study reports on optimization techniques using ANFIS approach such as genetic algorithms (GA), differential evolution (DE), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA) and satin bowerbird optimization algorithm (SBO) is also discussed. It is observed that most researchers developed ANFIS models to predict soil properties. We thus present proposals for future research. Overall, this study highlights the need for ANFIS to predict the stability of road embankment. Interestingly, we find that researchers successfully use the ANFIS model with the ability to predict with acceptable accuracy. Nevertheless, our findings also revealed that no researchers had done the use of ANFIS to predict slope stability and settlement. [ABSTRACT FROM AUTHOR]

Multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide diverse decisions to decision-makers in real world optimization problems. Many multimodal evolutionary mechanisms have been proposed to explore and exploit two solution spaces (i.e. decision space and objective space) in recent years. However, most existing methods only use single evolutionary operator to generate offsprings and ignore the advantage of using hybrid evolutionary algorithm. Moreover, it is still a great challenge to balance the effectiveness and efficiency simultaneously in the evolutionary process of MMO. In view of this, an efficient Two-Archive model based multimodal evolutionary algorithm is proposed in this paper. Two parallel offspring generation mechanisms based on competitive particle swarm optimizer and differential evolution are applied to expand two solution spaces with different evolutionary requirements. Moreover, niching local search scheme and reverse vector mutation strategy play roles in achieving better convergence and diversity. Finally, 22 MMO test problems are used to validate the superiority of the proposed method by comparing it with 5 state-of-the-art MMO algorithms. The proposed method is also expanded to solve 9 feature selection problems for validating the effectiveness of the proposed method on real world applications. • Two offspring generation mechanisms based on competitive PSO and DE are proposed. • Two diversity maintenance strategies named niching local search scheme and reverse vector mutation strategy are designed. • This paper proposes a novel multimodal multi-objective optimization algorithm with a new Two-Archive model. • Experiments validate that the proposed method can solve MMOPs effectively and efficiently with a relatively smaller population size. • The proposed algorithm is expanded to deal with 9 real world feature selection problems. [ABSTRACT FROM AUTHOR]

This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs). A fault discriminator is constructed to distinguish the type of faults once they are detected. After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults. The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters. Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components. The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle. [ABSTRACT FROM PUBLISHER]

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree ( T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts. [ABSTRACT FROM AUTHOR]

The differential evolution (DE) and harmony search (HS) are two well-known nature-inspired computing techniques. Both of them can be applied to effectively cope with nonlinear optimization problems. In this paper, we propose and study a new DE method, DE-HS, by utilizing the fresh individual generation mechanism of the HS. The HS-based approach can enhance the local search capability of the original DE. Optimization of some unconstrained and constrained benchmark problems and a real-world wind generator demonstrate that our DE-HS has an improved convergence property. [ABSTRACT FROM AUTHOR]

The feature selection problem often occurs in pattern recognition and, more specifically, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features, which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold crossvalidation on the archive solutions and selecting the best one. Experimental analysis is conducted on several standard test sets. The classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis shows that the proposed approach successfully determines good feature subsets which may increase the classification accuracy. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution. [Copyright &y& Elsevier]

In this paper, a novel hybrid algorithm based on artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm called ABC-DE is proposed to inherit their advantages and overcome their drawbacks. In ABC-DE algorithm, employed bees employ the mutation and crossover strategies of DE to enforce their exploration ability while onlooker bees keep their original updating strategy to retain the exploitation ability. Classical test functions have been provided to testify the ABC-DE algorithm. Furthermore, ABC-DE is applied to different types of time-modulated arrays (TMAs) pattern synthesis problems, including the syntheses of low equal-ripple sidelobe level (SLL) pattern, deep null level pattern, multiple-beams patterns and satellite footprint pattern. Experiment results reveal that ABC-DE has a promising performance in the convergence rate and the exploration ability when compared with other improved evolutionary algorithms, which indicates that the proposed algorithm can be an effective approach for TMAs synthesis problems. [ABSTRACT FROM PUBLISHER]

This paper presents a new optimization tool for analog circuit design. The proposed tool is based on the robust version of the differential evolution optimization method. Corners of technology, temperature, voltage and current supplies are taken into account during the optimization. That ensures robust resulting circuits. Those circuits usually do not need any schematic change and are ready for the layout. The newly developed tool is implemented directly to the Cadence design environment to achieve very short setup time of the optimization task. The design automation procedure was enhanced by optimization watchdog feature. It was created to control optimization progress and moreover to reduce the search space to produce better design in shorter time. The optimization algorithm presented in this paper was successfully tested on several design examples. [ABSTRACT FROM AUTHOR]

As the foremost step of spectral unmixing, endmember extraction has been one of the most challenging techniques in the spectral unmixing processing due to the mixing of pixels and the complexity of hyperspectral remote sensing images. The existing geometrial-based endmember extraction algorithms have achieved the ideal results, but most of these algorithms perform poorly when they do not meet the assumption of simplex structure. Recently, many intelligent optimization algorithms have been employed to solve the problem of endmember extraction. Although they achieved the better performance than the geometrial-based algorithms in different complex scenarios, they also suffer from the time-consuming problem. In order to alleviate the above problems, balance the two key indicators of accuracy and running time, an adaptive surrogate-assisted endmember extraction (ASAEE) framework based on intelligent optimization algorithms is proposed for hyperspectral remote sensing images in this paper. In the proposed framework, the surrogate-assisted model is established to reduce the expensive time cost of the intelligent algorithms by fitting the fully constrained evaluation value with the low-cost estimated value. In more detail, three commonly used intelligent algorithms, namely genetic algorithm, particle swarm optimization algorithm and differential evolution algorithm, are specifically designed into the ASAEE framework to verify the effectiveness and robustness. In addition, an adaptive weight surrogate-assisted model selection strategy is proposed, which can automatically adjust the weights of different surrogate models according to the characteristics of different intelligent algorithms. Experimental results on three data sets (including two simulated data sets and one real data set) show the effectiveness and the excellent performance of the proposed ASAEE framework. [ABSTRACT FROM AUTHOR]

Parameter optimization in the field of control engineering has always been a research topic. This paper studies the parameter optimization of an active disturbance rejection controller. The parameter optimization problem in controller design can be summarized as a nonlinear optimization problem with constraints. It is often difficult and complicated to solve the problem directly, and meta-heuristic algorithms are suitable for this problem. As a relatively new method, the ant-lion optimization algorithm has attracted much attention and study. The contribution of this work is proposing an adaptive ant-lion algorithm, namely differential step-scaling ant-lion algorithm, to optimize parameters of the active disturbance rejection controller. Firstly, a differential evolution strategy is introduced to increase the diversity of the population and improve the global search ability of the algorithm. Then the step scaling method is adopted to ensure that the algorithm can obtain higher accuracy in a local search. Comparison with existing optimizers is conducted for different test functions with different qualities, the results show that the proposed algorithm has advantages in both accuracy and convergence speed. Simulations with different algorithms and different indexes are also carried out, the results show that the improved algorithm can search better parameters for the controllers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

There has been much work in establishing joint replenishment model and designing effective and robust algorithms. Little research has been done by direct grouping methods. In this paper, we present a differential evolution (DE) algorithm that uses direct grouping to solve joint replenishment problem (JRP). Extensive computational experiments are performed to compare the performances of the DE algorithm with results of evolutionary algorithm (GA). The experimental results indicate that the DE algorithm can find a replenishment policy that incurs a lower total cost than the GA. We also conducted a case study to test the proposed DE algorithm for the JRP. The findings suggest that the proposed model is successful in decreasing spare parts ordering costs and holding costs significantly in a power plant. [ABSTRACT FROM AUTHOR]

The prime objective of a Channel Assignment Problem (CAP) is to assign appropriate number of required channels to each cell in a way to achieve both efficient frequency spectrum utilization and minimization of interference effects (by satisfying a number of channel reuse constraints). Dynamic Channel Assignment (DCA) assigns the channels to the cells dynamically according to traffic demand, and hence, can provide higher capacity (or lower call blocking probability), fidelity and quality of service than the fixed assignment schemes. Channel assignment algorithms are formulated as combinatorial optimization problems and are NP-hard. Devising a DCA, that is practical, efficient, and which can generate high quality assignments, is challenging. Though Metaheuristic Search techniques like Evolutionary Algorithms, Differential Evolution, Particle Swarm Optimization prove effective in the solution of Fixed Channel Assignment (FCA) problems but they still require high computational time and therefore may be inefficient for DCA. A number of approaches have been proposed for the solution of DCA problem but the high complexity of these proposed approaches makes them unsuitable/less efficient for practical use. Therefore, this paper presents an effective and efficient Hybrid Discrete Binary Differential Evolution Algorithm (HDB-DE) for the solution of DCA Problem. [ABSTRACT FROM AUTHOR]

In this research paper we present an immunological algorithm (IA) to solve global numerical optimization problems for high-dimensional instances. Such optimization problems are a crucial component for many real-world applications. We designed two versions of the IA: the first based on binary-code representation and the second based on real values, called opt-IMMALG01 and opt-IMMALG, respectively. A large set of experiments is presented to evaluate the effectiveness of the two proposed versions of IA. Both opt-IMMALG01 and opt-IMMALG were extensively compared against several nature inspired methodologies including a set of Differential Evolution algorithms whose performance is known to be superior to many other bio-inspired and deterministic algorithms on the same test bed. Also hybrid and deterministic global search algorithms (e.g., DIRECT, LeGO, PSwarm) are compared with both IA versions, for a total 39 optimization algorithms.The results suggest that the proposed immunological algorithm is effective, in terms of accuracy, and capable of solving large-scale instances for well-known benchmarks. Experimental results also indicate that both IA versions are comparable, and often outperform, the state-of-the-art optimization algorithms. [ABSTRACT FROM AUTHOR]

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The main goal in this paper is to design single-mode optical fibers for DWDM networks, which are used today in rapid communications. These networks require low dispersion in a wide range of wavelengths. So, in this paper, multi-layer optical fibers with low dispersion value and flat dispersion slope in wavelength range of (1.5-1.6μm) are designed, using optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model named: CLA-DE for global numerical optimization. This model is based on cellular learning automata (CLA) and differential evolution algorithm. The main idea is to learn the most promising regions of the search space using cellular learning automata. Learning automata in the CLA iteratively partition the search dimensions of a problem and learn the most admissible partitions. In order to facilitate incorporation among the CLA cells and improve their impact on each other, differential evolution algorithm is incorporated, by which communication and information exchange among neighboring cells are speeded up. The proposed model is compared with some evolutionary algorithms to demonstrate its effectiveness. Experiments are conducted on a group of benchmark functions which are commonly used in the literature. The results show that the proposed algorithm can achieve near optimal solutions in all cases which are highly competitive with the ones from the compared algorithms. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q\% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. Since its inception in the mid 1990s, DE has been finding many successful applications in real-world optimization problems from diverse domains of science and engineering. This paper takes a first significant step toward the convergence analysis of a canonical DE (DE/rand/1/bin) algorithm. It first deduces a time-recursive relationship for the probability density function (PDF) of the trial solutions, taking into consideration the DE-type mutation, crossover, and selection mechanisms. Then, by applying the concepts of Lyapunov stability theorems, it shows that as time approaches infinity, the PDF of the trial solutions concentrates narrowly around the global optimum of the objective function, assuming the shape of a Dirac delta distribution. Asymptotic convergence behavior of the population PDF is established by constructing a Lyapunov functional based on the PDF and showing that it monotonically decreases with time. The analysis is applicable to a class of continuous and real-valued objective functions that possesses a unique global optimum (but may have multiple local optima). Theoretical results have been substantiated with relevant computer simulations. [ABSTRACT FROM AUTHOR]

Abstract In previous optimization methods for multi-module satellite equipment (component) layout optimization problem, each component was limited to certain module or supporting surface and could only search its position there. Components could not migrate from one module or supporting surface to another. In this case, the layout design of components within satellite module was seriously hindered from further improvement. In this study a component assignment and layout integration optimization algorithm is presented to deal with this problem, which can assign components to each module of satellite dynamically during optimization procedure. The aim of this paper is to expand the solution space of component layout optimization so as to further improve the component layout design. The proposed component assignment and layout integration optimization algorithm is inspired from the idea of stepwise regression in multiple regression analysis, which allows independent variables to enter or leave regression equation freely. In the proposed algorithm components enter the satellite module one by one in descending order of the product of mass and height. For all supporting surfaces within satellite module, each component will try all of them through layout optimization together with these components have been in the satellite module, and finally select the one with the best fitness as its initial assignment. At the same time, these components have been in the satellite module will be evaluated by their moment of inertia to decide whether they leave the current supporting surfaces and move to another or not. The layout optimization algorithm uses the differential evolution (DE) and random mutation operation to optimize the coordinates and orientations of components, respectively. The performance of the proposed algorithm is finally evaluated on a simplified satellite case. Experimental results show the proposed algorithm outperforms other two algorithms that did not consider component assignment in computational accuracy. Graphical abstract Highlights • Multi-module satellite layout problem usually did not consider component assignment. • We propose a component assignment algorithm based on the idea of stepwise regression. • Components outside of satellite module enter the module one by one in a proper order. • Components in the module are allowed to migrate from one sub-module to another. • Whether a component enters or leaves a sub-module depends on the results of layout optimization. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE) is a widely used successful evolutionary algorithm (EA) based on a population of individuals, which is especially well suited to solve problems that have non-linear, multimodal cost functions. However, for a given population, the set of possible new populations is finite and a true subset of the cost function domain. Furthermore, the update formula of DE does not use any information about the fitness of the population. This paper presents a novel extension of DE called Randomized and Rank-based Differential Evolution (R2DE) and its self-adaptive version SAR2DE to improve robustness and global convergence speed on multimodal problems by introducing two multiplicative terms in the DE update formula. The first term is based on a random variate of a Cauchy distribution, which leads to a randomization. The second term is based on ranking of individuals, so that R2DE exploits additional information provided by the population fitness. In extensive experiments conducted with a wide range of complexity settings, we show that the proposed heuristics lead to an overall improvement in robustness and speed of convergence compared to several global optimization techniques, including DE, Opposition based Differential Evolution (ODE), DE with Random Scale Factor (DERSF) and the self-adaptive Cauchy distribution based DE (NSDE). [ABSTRACT FROM AUTHOR]

Abstract: This paper presents combination of differential evolution (DE) and biogeography-based optimization (BBO) algorithm to solve complex economic emission load dispatch (EELD) problems of thermal generators of power systems. Emission substances like NO X , SO X , COX, Power demand equality constraint and operating limit constraint are considered here. Differential evolution (DE) is one of the very fast and robust, accurate evolutionary algorithms for global optimization and solution of EELD problems. Biogeography-based optimization (BBO) is another new biogeography inspired algorithm. Biogeography deals with the geographical distribution of different biological species. This algorithm searches for the global optimum mainly through two steps: migration and mutation. In this paper combination of DE and BBO (DE/BBO) is proposed to accelerate the convergence speed of both the algorithm and to improve solution quality. To show the advantages of the proposed algorithm, it has been applied for solving multi-objective EELD problems in a 3-generator system with NO X and SO X emission, in a 6-generators system considering NO X emission, in a 6-generator system addressing both valve-point loading and NO X emission. The current proposal is found better in terms of quality of the compromising and individual solution obtained. [Copyright &y& Elsevier]

Abstract: This paper proposes two hybrid algorithms, one between particle swarm optimization (PSO) and differential evolution (DE) and second between PSO and quantum infusion (QI). This paper applies these algorithms for digital filter design. PSO algorithm is used as a basis for comparison. Extensive simulation results show the superiority of algorithms developed in this paper. [Copyright &y& Elsevier]

Abstract: In this paper a new dynamic model for forecasting electricity prices from 1 to 24h in advance is proposed. The model is a dynamic filter weight Adaline using a sliding mode weight adaptation technique. The filter weights for this neuron constitute of first order dynamic filter with adjustable parameters. Sliding mode invariance conditions determine a least square characterization of the adaptive weights average dynamics whose stability features may be studied using standard time varying linear system results. The approach is found to exhibit robustness characteristics and first convergence properties. Comparison of results with a local linear wavelet neural network model is also presented in this paper. The hourly electricity prices of California and Spanish energy markets are taken as experimental data and the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are computed to find out the forecasting performance of both the models. In both the cases the MAPE and RMSE are found to be within the tolerable limits. As dynamic filter weight neural network gives better results in comparison to local linear wavelet neural network, the former has been further integrated with differential evolution algorithm to enhance the performance. [Copyright &y& Elsevier]

This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques. [ABSTRACT FROM AUTHOR]

The Gravitational Search Algorithm (GSA) has excellent performance in solving various optimization problems. However, it has been demonstrated that GSA tends to trap into local optima and are easy to lose diversity in the late evolution process. In this paper, a new hybrid algorithm based on self-adaptive Gravitational Search Algorithm (GSA) and Differential Evolution (DE) is proposed for solving single objective optimization, named SGSADE. Firstly, a self-adaptive mechanism based on GSA is proposed for improving the convergence speed and balancing exploration and exploitation. Secondly, the diversity of the population is maintained in the evolution process by using crossover and mutation operation from DE. Besides, to improve the performance of the algorithm, a new perturbation based on Levy flight theory is embedded to enhance exploitation capacity. The simulated results of SGSADE on 2017 CEC benchmark functions show that the SGSADE outperforms the state-of-the-art variant algorithms of the GSA. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a decomposition-based multi-objective optimization approach considering multiple preferences, expressed by means of reference points, and with robust performance (mprMOEA/D). This algorithm is able to find multiple preferred regions in a single run, and its performance is robust with respect to different problems. The proposed algorithm utilizes a subpopulation (SP) for each reference point to search for the corresponding preferred region. An external population (EP) is maintained to selectively preserve solutions from all the SPs, and it can be revisited when producing new solution for each SP. The proposed collaboration mechanism between the SPs and EP is helpful in convergence and diversity preserving. In order to obtain robust performance, local crossover coordinate systems, which coincide with the local manifold of the Pareto set, are introduced into mprMOEA/D for the crossover operator of differential evolution, alleviating the influence of the overall Pareto set shape. The effects of these adopted techniques on the proposed algorithm are discussed, and the robust performance of the proposed approach is validated using numerical functions in comparison with four existing approaches. Experimental results show that the proposed algorithm outperforms the other algorithms. Highlights • A MOEA approach that can find multiple regions of interest is proposed. • A collaboration mechanism between subpopulations and an external one is used. • Local coordinate crossover operator for DE is introduced. [ABSTRACT FROM AUTHOR]

In this paper, an improved version of the Grey Wolf Optimizer (GWO) is proposed to improve the exploration and the exploitation ability of the GWO algorithm. This improvement is performed through using the chaotic logistic map, the Opposition-Based Learning (OBL), the differential evolution(DE), and the disruption operator (DO). Where, the chaotic logistic map and the OBL are used to initialize the candidate solutions and these approaches avoid the drawbacks of the random population and increase the convergence of the algorithm. Then, the DE operators are combined with the GWO algorithm, in which, the DE operators work as a local search mechanism to improve the exploitation ability of the GWO through updating the population. Also, after updating the solutions by using a hybrid between the GWO and the DE, the DO is used to enhance the exploration ability, in which, the DO is used to maintain the diversity of the population. Therefore, the combinations with chaotic logistic map, OBL, DE, and DO, provide the GWO with tools to better balance between the exploration and the exploitation of the search space without affecting the computational time required for this task. The proposed algorithm, called COGWO2D, is compared with other seven algorithms through a set of experimental series that have been performed over two benchmark functions, the classical CEC2005, and the CEC2014. Also, the performance of the proposed algorithm to improve the classification of the galaxy images is evaluated, where it is used as a feature selection method. The aim of this experiment is to select the optimal subset of features from the extracted features of the galaxy images. The experimental results support the efficacy of the proposed approach to find the optimal solutions of the global optimization problem, as well as, increase the accuracy of the classification of the galaxy images. [ABSTRACT FROM AUTHOR]

Highlights • A self-adaptive differential evolution optimization method is proposed. • The proposed method enhances the convergence speed and the exploitation capabilities. • The performance of the proposed method outperforms the original differential evolution method. • The operational parameters in ES-SAGD process are optimized to achieve maximal NPV. Abstract Differential evolution (DE) algorithm has shown good performance in many optimization problems. However, its control parameters greatly affect its performance and require many trials to determine the optimum values of control parameters for each specific optimization problem. In this paper, we present a self-adaptive DE with a new adaptation technique to improve the solution quality as well as increase the speed of convergence with a reduction in the computational cost. The proposed approach, called modified self-adaptive differential evolution (MSaDE), employs a new success-rate indicator of the strategies used to generate the trial vectors in conventional self-adaptive differential evolution (SaDE) algorithm. The proposed method has been tested on 22 benchmark problems and on the expanded solvent steam-assisted gravity drainage (ES-SAGD) recovery process. The results show that a significant speed-up is achieved in the exploitation and exploration capabilities of the self-adaptive DE algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the utilization of two evolutionary algorithms Self-Organizing Migrating Algorithm (SOMA) and Differential Evolution (DE) for the optimization of the control of chaos. This paper is aimed at an explanation on how to use evolutionary algorithms (EAs) and how to properly define the advanced targeting cost function (CF) securing fast, precise and mainly robust stabilization of selected chaotic system on a desired state for any initial conditions. The role of EA here is as a powerful tool for an optimal tuning of control technique input parameters. As a model of deterministic chaotic system, the one-dimensional discrete Logistic equation was used. The four canonical strategies of SOMA and six canonical strategies of DE were utilized. For each EA strategy, repeated simulations were conducted to outline the effectiveness and robustness of used method and targeting CF securing robust solution. Satisfactory results obtained by both heuristic and the two proposed cost functions are compared with previous research, given by different cost function designs. [ABSTRACT FROM AUTHOR]

This paper presents an improved differential evolution (IDE) method for the solution of large-scale unit commitment (UC) problems. The objective of the proposed scheme is to determine the generation schedule which minimizes the total operating cost over a given time horizon subject to a variety of constraints. Through its use of enhanced acceleration and migration processes, the IDE method limits the population size required in the search procedure and is therefore an ideal candidate for the solution of large-scale combinatorial optimization problems. The effectiveness of the proposed approach is verified by performing a series of simulations based upon the practical Tai-Power System (TPS) and various other power systems presented in the literature. In general, the results show that the IDE scheme outperforms existing deterministic and stochastic optimization methods both in terms of the quality of the solutions obtained and the computational cost. Furthermore, it is found that the magnitude of the cost savings achieved by the IDE scheme compared to that obtained by the other optimization techniques increases as the number of generating units within the power system increases. Therefore, the proposed scheme represents a particularly effective technique for the solution of large-scale UC problems. [ABSTRACT FROM AUTHOR]

This paper deals with the parameter determination procedure of a gas-discharge arrester's (GDA) model. As the accuracy of the model depends on its parameters, the goal of this procedure is to determine adequate parameter values so that the model's performances match the real device behavior as much as possible. In order to achieve this goal, differential evolution (DE), a stochastic optimization algorithm, is included in the determination process. The main task of the DE algorithm is to vary the model's parameters during the optimization process, thus minimizing the difference between the measured and model-calculated current and voltage time behavior. An accurate GDA model has been obtained on the basis of measurements on a single GDA test object. This model is relevant and suitable for a series of GDA devices as the agreement between their measured and model-calculated voltage and current time behavior is excellent. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid global optimization method referred to as DESA. The algorithm exploits random sampling and the metropolis criterion from simulated annealing to perform global search. The population of points and efficient search strategy of differential evolution are used to speed up the convergence. The algorithm is easy to implement and has only a few parameters. The theoretical global convergence is established for the hybrid method. Numerical experiments on 23 mathematical test functions have shown promising results. The method was also integrated into SPICE OPUS circuit simulator to evaluate its practical applicability in the area of analog integrated circuit sizing. Comparison was made with basic simulated annealing, differential evolution, and a multistart version of the constrained simplex method. The latter was already a part of SPICE OPUS and produced good results in past research. [ABSTRACT FROM AUTHOR]

In this article we propose a new method to calibrate directly the camera by which it was taken an image of a cuboid, and to find at the same time the orientation and side lengths of the cuboid. This is a highly non-linear optimization problem that is solved directly using a heuristic called differential evolution. We show in this paper that this problem is very difficult if one tries to solve it with a conventional scalar optimization procedure. Although differential evolution is a heuristic, we find valid results in 100% of the executions. We test our method with synthetic and real images. [ABSTRACT FROM AUTHOR]

Load flow analysis is an essential tool for the reliable planning and operation of interconnected power systems. The constant increase in power demand, apart from the increased intermittency in power generation due to renewable energy sources without proportionate augmentation in transmission system infrastructure, has driven the power systems to function nearer to their limits. Though the power flow (PF) solution may exist in such circumstances, the traditional Newton–Raphson based PF techniques may fail due to computational difficulties owing to the singularity of the Jacobian Matrix during critical conditions and faces difficulties in solving ill-conditioned systems. To address these problems and to assess the impact of large-scale photovoltaic generator (PVG) integration in power systems on power flow studies, a derivative-free quasi-oppositional heap-based optimization (HBO) (QOHBO) technique is proposed in the present paper. In the proposed approach, the concept of quasi-oppositional learning is applied to HBO to enhance the convergence speed. The efficacy and effectiveness of the proposed QOHBO-PF technique are verified by applying it to the standard IEEE and ill-conditioned systems. The robustness of the algorithm is validated under the maximum loadability limits and high R/X ratios, comparing the results with other well-known methods suggested in the literature. The results thus obtained show that the proposed QOHBO-PF technique has less computation time, further enhancement of reliability in the presence of PVG, and has the ability to provide multiple PF solutions that can be utilized for voltage stability analysis. [ABSTRACT FROM AUTHOR]

Abstract: The complexity of production scheduling problem in cold rolling line is analyzed, which is formulated as two parts, namely, the coil-merging optimization and the rolling batch planning. The optimization of steel coil merging is constructed as a multiple container packing problem (MCPP) that is computed by a new proposed algorithm, discrete differential evolution (DDE), in this paper. A specific double traveling salesman problem (DTSP) is modeled for the rolling batch planning, and a hybrid heuristic method on the basis of evolutionary mechanism and local search is presented to solve this model. The experimental results with real production data from Shanghai Baosteel Co. Ltd. show that the production scheduling method suggested in this paper is effective. [Copyright &y& Elsevier]

Abstract: The concept of particle swarms, although initially introduced for simulating human social behaviors, has become very popular these days as an efficient means for intelligent search and optimization. The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually very simple. This paper investigates a novel approach to the designing of two-dimensional zero phase infinite impulse response (IIR) digital filters using the PSO algorithm. The design task is reformulated as a constrained minimization problem and is solved by a modified PSO algorithm. Numerical results are presented. The paper also demonstrates the superiority of the proposed design method by comparing it with two recently published filter design methods and two other state of the art optimization techniques. [Copyright &y& Elsevier]

Results from our optimization exercise clearly show the advantage of using the random search algorithms when we anticipate the search for the global optimum to be difficult. When the number of parameters in the model is relatively small (nine parameters) Differential Evolution performs better than Genetic Algorithm. However, when the number of parameters in the model is relatively large (fifteen parameters) the reverse case is true. A comparison of the Quasi-Newton and Simplex methods also shows that both the Quasi-Newton algorithm of shazam and the simplex algorithm of fminsearch are sensitive to starting values. However, allowing shazam to set its starting values or using the PRESAMP option to set the starting values produced the best results for shazam. The general conclusion of this paper is that the choice of optimization technique for difficult optimization problems like the one attempted here should be based on problem attributes. When in doubt, multiple techniques should be applied and the estimated results evaluated. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a massive growth in the field of biologically inspired global search heuristics. Computational cost having been reduced almost dramatically, researchers from all corners are taking more interset in following the underlying principles of nature to solve nearly intractable search problems. In this paper, we attempt to solve one very important optimization problem arising in the field of two-dimensional IIR (infinite impulse response) filter design, with three naturally inspired global search algorithms. We have used a state-of-the-art real coded genetic algorithm (GA), one very recent and modified version of the particle swarm opimization (PSO) and finally an improved version of the differential evolution (DE) algorithm. The DE algorithm has been modified by us to prevent its premature convergence to some suboptimal region of the search space. The design task is formulated as a constrained minimization problem and solved by the three metaheuristics. Numerical results are presented over three difficult instances of the design problem. The study also compares the results with two recently published filter design methods. Our experiments reveal that the DE family of algorithms should receive primary attention in solving the constrained multidimensional filter design tasks. [ABSTRACT FROM AUTHOR]

Medical images are extremely valuable and sensitive data because of their significance in research, diagnosis, and treatment. However, the transmission of medical images over an unsecured network can lead to security issues such as modification, removal, copying, and unauthorized access. For this reason, the protection of medical information is of great importance. This paper proposes a novel reversible fragile digital watermarking method for tamper localization and authenticity and integrity protection in medical images. In the proposed method, the host image is firstly divided into 8 × 8 non-overlapped blocks. Then the image is transformed to the frequency domain by applying a Discrete Wavelet Transform, and the watermark information is embedded. Finally, the image is converted from the frequency domain to the spatial domain using an inverse Discrete Wavelet Transform. During this transformation, an intelligent optimization technique is used to correct the rounding errors and obtain the watermarked image with the highest level of imperceptibility. The experimental results demonstrate that the proposed method offers a watermarked image with high imperceptibility, extracts the host image lossless during the watermark extraction phase, and performs tamper localization with high precision. [Display omitted] • The proposed fragile image watermarking method offers precise tamper localization. • Blind watermark extraction is provided, no storage needed for host image and watermark. • The method is reversible, which makes it appropriate for e-healthcare applications. • Differential Evolution Algorithm is used to address rounding faults in domain transformations. [ABSTRACT FROM AUTHOR]

In this study, parameter adaptation methods for differential evolution are automatically designed using a surrogate approach. In particular, Taylor series are applied to model the searched dependence between the algorithm's parameters and values, describing the current algorithm state. To find the best-performing adaptation technique, efficient global optimization, a surrogate-assisted optimization technique, is applied. Three parameters are considered: scaling factor, crossover rate and population decrease rate. The learning phase is performed on a set of benchmark problems from the CEC 2017 competition, and the resulting parameter adaptation heuristics are additionally tested on CEC 2022 and SOCO benchmark suites. The results show that the proposed approach is capable of finding efficient adaptation techniques given relatively small computational resources. [ABSTRACT FROM AUTHOR]

The economic emission dispatch problem (EEDP) is a nonconvex and nonsmooth multiobjective optimization problem in the power system field. Generally, fuel cost and total emissions of harmful gases are the problem objective functions. The EEDP decision variables are output powers of thermal generating units (TGUs). To make the EEDP problem more practical, valve point loading effects (VPLEs), prohibited operation zones (POZs), and power balance constraints should be included in the problem constraints. In order to solve this complex and constrained EEDP, a new multiobjective optimization technique combining the differential evolution (DE) algorithm and chaos theory is proposed in this study. In this new multiobjective optimization technique, a nondomination sorting principle and a crowding distance calculation are employed to extract an accurate Pareto front. To avoid being trapped in local optima and enhance the conventional DE algorithm, two different chaotic maps are used in its initialization, crossover, and mutation phases instead of random numbers. To overcome difficulties caused by the equality constraint describing the power balance constraint, a slack TGU is defined to compensate for the gap between the total generation and the sum of the system load and total power losses. Then, the optimal power outputs of all thermal units except the slack unit are determined by the suggested optimization technique. To assess the effectiveness and applicability of the proposed method for solving the EEDP, the six-unit and ten-unit systems are used. Moreover, obtained results are compared with other new optimization techniques already developed and tested for the same purpose. The superior performance of the ChMODE is also evaluated by using various metrics such as inverted generational distance (IGD), hyper-volume (HV), spacing metric (SM), and the average satisfactory degree (ASD). [ABSTRACT FROM AUTHOR]