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Outer-rotor switched reluctance machines (SRMs) have drawn much attention as promising candidates for in-wheel direct-drive motors of future electric vehicles. This article presents a systematic performance comparison of three outer-rotor SRM topologies for in-wheel traction applications in terms of the specific torque, electromagnetic efficiency, torque ripple, radial force, and mechanical aspects. A generalized design optimization framework for SRMs is proposed to enable the fast evaluation of large numbers of designs generated from the differential evolution by incorporating an analytical current profile estimation into the transient finite element analysis. The relationship between the saliency ratio and converter volt-ampere rating is also discussed. The calculations are then benchmarked with the experimental results from an existing prototype. The effectiveness of the performance prediction method and the proposed optimization approach is validated. [ABSTRACT FROM AUTHOR]

Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This article discusses design and optimization of a permanent magnet (PM) motor for electric transmission oil pump (eTOP) applications. The PM motor is fed from the vehicle low voltage battery system (12 Vdc) and drives a mechanical pump assembly (MPA) in a generated-rotor (Gerotor) configuration. The eTOP is accommodated inside the transmission where it supplies required oil flows at specified pressures for lubrication and cooling purposes. The optimization is formulated as a multi-objective problem that is solved using a differential evolution (DE) algorithm. An existing PM motor is used as a benchmark machine against which an optimized solution for mass manufacturing considering real-world application constraints is proposed. A 3-phase winding approach that facilities a needle winding process is considered for the eTOP PM motor. Once the optimized solution is found the PM motor's performance is evaluated and presented. To validate the design a prototype PM motor with production materials and tooling is built and tested. The comparison of measured and predicted results shows good agreement. [ABSTRACT FROM AUTHOR]

In a joint source-channel coding (JSCC) system, excessive source compression can cause an error floor. In the original JSCC scheme based on double protograph LDPC (DP-LDPC) codes, only connections exist between check nodes (CNs) of the source protograph and variable nodes (VNs) of the channel protograph, and error floors are observed. In this paper, we investigate the joint protograph that forms the basis of a DP-LDPC code. The joint protograph consists of a source protograph, a channel protograph, and connections between the two protographs. Our main focus is on the complete-source-variable-linking (CSVL) protomatrix of the joint protograph, which determines the error floor of the DP-LDPC code. We propose a generalized source protograph extrinsic information transfer (GSP-EXIT) algorithm for evaluating the source decoding thresholds of the CSVL protomatrix. Based on the proposed algorithm, we analyze CSVL protomatrices with regular or irregular source protographs. We present design criteria for connections between VNs of the source protograph and CNs of the channel protograph. Such design rules will result in a higher source decoding threshold, which implies a lower error floor. We also propose a differential evolution algorithm for optimizing the source protograph. We present/compare analytical results and/with simulation results, and conclude that they are consistent. [ABSTRACT FROM AUTHOR]

When multiple leaks occur at the same time, the influence of leak acoustic wave signals is superposed in the leak acoustic wave signal, which affects the propagation and attenuation rule of the transient acoustic signal. The traditional leak localization method is impossible to effectively locate the multiple leak signals under the condition of mixed signal. Thus, a localization method of multiple leaks based on time-frequency analysis of acoustic wave and improved differential evolution is proposed. Since the decomposition model number of Variational Mode Decomposing (VMD) affects the feature extraction, the energy function is used to improve VMD (IVMD). Therefore, the decomposition model number of VMD is determined by energy function of each component. The IVMD is applied to acoustic wave analysis and processing. Additionally, the number of multiple leaks is extracted by Time-frequency Analysis (TFA). Then, the multiple leaks localization function is obtained. Differential Evolution (DE) is easy to fall into local optimum, and its convergence speed is slow in the later stage of evolution, Particle Swarm Optimization Algorithm (PSO) is used to improve the global convergence speed of DE, and the multiple leaks positions are calculated by improved differential evolution. By analyzing the acquired acoustic wave data of multiple leaks in experimental pipeline wtih acoustic sensor PCB 106B, the results show that the proposed method can accurately locate multiple leaks in pipeline, and the minimum error of leak localization is 18 m. [ABSTRACT FROM AUTHOR]

The increasing popularity of battery-limited electric vehicles puts forward an important issue of how to charge the vehicles effectively. This problem, commonly referred to as Electric Vehicle Charging Scheduling (EVCS), has been proven to be NP-hard. Most of the existing works formulate the EVCS problem simply as a constrained shortest path finding problem and treat it by discrete optimization. However, other variables such as the charging amount of energy and the charging option at a station need to be considered in practical use. This paper hence formulates the EVCS problem as a hierarchical mixed-variable optimization problem, considering the dependency among the station selection, the charging option at each station and the charging amount settings. To adapt to the new problem model, we specifically design a Mixed-Variable Differentiate Evolution (MVDE) as the scheduling algorithm for our proposed EVCS system. The MVDE contains several specific operators, including a charging station route construction, a hierarchical mixed-variable mutation operator and a constraint-aware evaluation operator. Experimental results validate the effectiveness of our proposed MVDE-based system on both synthetic and real-world transportation networks. [ABSTRACT FROM AUTHOR]

Many real-world problems contain a large number of decision variables which can be modeled as large scale global optimization (LSGO) problems. One effective way to solve an LSGO problem is to decompose it into smaller subproblems to solve. The existing works mainly focused on designing methods to decompose separable problems, while seldom focused on the decomposition of nonseparable large scale problems. Also, the existing decomposition methods only learn the interaction (correlation or interdependence) among variables to make the decomposition. In this article, we make the decomposition deeper: we not only consider the variable interaction but also take the essentialness of the variable into account to form a deep grouping method. To do this, we first design an essential/trivial variable detection scheme to support the deep decomposition for both separable problems and nonseparable problems. Based on it, we propose a new decomposition method called deep grouping method. Then, we design a new differential evolution (DE) algorithm with a new mutation strategy. By integrating all these, we propose a hybrid deep grouping (HDG) algorithm. Finally, the experiments are conducted on the widely used and most challenging LSGO benchmark suites, and the comparison results of the proposed algorithm with the state-of-the-art algorithms indicate the proposed algorithm is more effective. [ABSTRACT FROM AUTHOR]

The emergence of fuzzy sets makes job-shop scheduling problem (JSSP) become better aligned with the reality. This article addresses the JSSP with fuzzy execution time and fuzzy completion time (FJSSP). We choose the classic differential evolution (DE) algorithm as the basic optimization framework. The advantage of the DE algorithm is that it uses a special evolutionary strategy of difference vector sets to carry out mutation operation. However, DE is not very effective in solving some instances of FJSSP. Therefore, we propose a novel selection mechanism augmenting the generic DE algorithm (NSODE) to achieve better optimization results. The proposed selection operator adopted in this article aims at a temporary retention of all children generated by the parent generation, and then selecting N better solutions as the new individuals from N parents and N children. Various examples of fuzzy shop scheduling problems are experimented with to test the performance of the improved DE algorithm. The NSODE algorithm is compared with a variety of existing algorithms such as ant colony optimization, particle swarm optimization, and cuckoo search. Experimental results show that the NSODE can obtain superior feasible solutions compared with solutions produced by several algorithms reported in the literature. [ABSTRACT FROM AUTHOR]

Unlike most studies assuming exact knowledge of the transmit power and path-loss exponent, we address the localization problem under a more practical assumption that the exact values of the transmit power and/or path-loss exponent are unavailable. We propose an algorithm based on the differential evolution, opposition based learning, and adaptive redirection that jointly estimates the position of the target node and the parameters. Results from simulation and indoor experiments show that the proposed algorithm provides higher localization accuracy and requires less computational time than conventional algorithms especially when the exact values of the transmit power and path-loss exponent are unavailable simultaneously. [ABSTRACT FROM AUTHOR]

Series resonant converter (SRC) is one of the main parts which has to be designed carefully to be compatible with the general requirements of photovoltaic (PV) grid-connected system. The extensive search space for design parameters of SRC such as resonant tank component values, transformer turn ratio, dead time and switching frequency requires an automated design framework. The proposed framework is developed using genetic algorithm (GA), differential evolution algorithm (DEA), and nondominated sorting genetic algorithm (NSGA-II) for the multi-objective optimization of SRC circuit while accomplishing zero voltage switching (ZVS). Regulating the output voltage while minimizing the losses of MOSFET, diode and transformer are the design objectives. FEM is applied to subdivide the single phase transformer into simpler and finite parts to calculate flux density value according to the optimized design parameters. Amongst these evolutionary algorithms, NSGA-II achieved the best optimization performance considering various operation modes and required specifications. Simulation results are also provided to validate the efficient and robust automated design of SRC to be used in photovoltaic systems. [ABSTRACT FROM AUTHOR]

Deep belief network (DBN) is one of the most representative deep learning models. However, it has a disadvantage that the network structure and parameters are basically determined by experiences. In this article, an improved quantum-inspired differential evolution (MSIQDE), namely MSIQDE algorithm based on making use of the merits of the Mexh wavelet function, standard normal distribution, adaptive quantum state update, and quantum nongate mutation, is proposed to avoid premature convergence and improve the global search ability. Then, the MSIQDE with global optimization ability is used to optimize the parameters of the DBN to construct an optimal DBN model, which is further applied to propose a new fault classification, namely MSIQDE-DBN method. Finally, the vibration data of rolling bearings from the Case Western Reserve University and a real-world engineering application are carried out to verify the performance of the MSIQDE-DBN method. The experimental results show that the MSIQDE takes on better optimization performance, and the MSIQDE-DBN can obtain higher classification accuracy than the other comparison methods. [ABSTRACT FROM AUTHOR]

To deal with the multimodality, nonlinearity, and nonconvexity of the maximum likelihood (ML) cost function when solving the localization problem with received signal strength (RSS) in wireless sensor networks, we propose a localization approach based on the differential evolution algorithm, opposition-based learning, and adaptive redirection in a unified way. In the proposed approach, we need neither to approximate the ML cost function nor to provide a good initial point while most conventional approaches, including those based on the semidefinite programming, second-order cone programming, and unscented transformation. The performance and computational complexity of the proposed approach are analyzed and compared with those of other approaches for three practical scenarios. Simulation results confirm that the proposed approach performs better than other approaches with relatively low computational complexity. Note to Practitioners—This article was motivated by the problem of localization in wireless sensor networks that can be found in different areas, such as intelligent farming, health care monitoring, and environmental monitoring. In this article, a technique is proposed, which exploits the RSS measurements to estimate the position of a target node. This technique is quite popular due to its simplicity and low cost. Most of the techniques based on the ML formulation approximate the ML cost function due to its high nonlinearity and nonconvexity. This article suggests a new approach using differential evolution and opposition-based learning a redirection, which does not require approximating the ML cost function to find the location of a target. In addition, in this article, we analyze the sensor nodes’ placement and its effect on localization accuracy. Numerical results in three practical scenarios are provided to demonstrate the effectiveness of the proposed approach and its superiority compared with state-of-the-art algorithms. In future research, we will address a more realistic scenario with multiple targets. [ABSTRACT FROM AUTHOR]

A cloud computing paradigm has quickly developed and been applied widely for more than ten years. In a cloud data center, cloud service providers offer many kinds of cloud services, such as virtual machines (VMs), to users. How to achieve the optimized allocation of VMs for users to satisfy the requirements of both users and providers is an important problem. To make full use of VMs for providers and ensure low makespan of user tasks, we formulate an optimal allocation model of VMs and develop an improved differential evolution (IDE) method to solve this optimization problem, given a batch of user tasks. We compare the proposed method with several existing methods, such as round-robin (RR), min–min, and differential evolution. The experimental results show that it can more efficiently decrease the cost of cloud service providers while achieving lower makespan of user tasks than its three peers. Note to Practitioners—VM allocation is one of the challenging problems in cloud computing systems, especially when user task makespan and cost of cloud service providers have to be considered together. We propose an IDE approach to solve this problem. To show its performance, this article compares the commonly used methods, i.e., RR and min–min, as well as the classic differential evolution method. A cloud simulation platform called CloudSim is used to test these methods. The experimental results show that the proposed one can well outperform its compared ones, and its VM allocation results can achieve the highest satisfaction of both users and providers. The proposed method can be readily applicable to industrial cloud computing systems. [ABSTRACT FROM AUTHOR]

Recently dynamic social networks witnessed a massive surge in popularity, especially in the area of anomaly detection. Although the text-based methods have achieved impressive detection performances, their applications are limited to the social text provided by users. This research focuses on graph-based methods and proposes a universal method for generalized social networks. Different from the existing graph-based methods that summarize a number of structural features, the proposed nodes' evolution diversity inspired method (NEDM) detects anomalies in dynamic social networks from the perspective of diverse evolution mechanisms. More specifically, NEDM applies link prediction algorithms at the micro-level to fit evolution mechanisms followed by the behaviors of nodes, and designs indices to evaluate their fitting degrees in edge removal and generation processes. In addition, the behavior of a node is represented as a quantum superposition state where such behavior follows different evolution mechanisms with uncertain probabilities. We propose a quantum mechanism based particle swarm optimization algorithm (QMPSO) in NEDM. QMPSO determines the optimal observation states of the behaviors of different nodes, and maximally reflects the evolutional fluctuations in the evolution processes of social networks. As a result, NEDM can quantify the evolutional fluctuations in different periods, and detect anomalies in dynamic social networks. Comparing with art-of-the-state methods and real social data in extensive experiments on disparate real-world social networks, we verify the outstanding performance of NEDM in terms of both accuracy and universality. [ABSTRACT FROM AUTHOR]

Overall constraint violation functions are commonly used in multiobjective evolutionary algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck in two stagnation states: 1) since the feasible region of a multiobjective optimization problem can consist of several disconnected feasible subregions, the search can be easily trapped in a feasible subregion which does not contain all the global Pareto optimal solutions and 2) an overall constraint violation function may have many nonzero minimal points, it can make the search stuck in an unfeasible area. To address these two issues, this article proposes a strategy to detect whether or not the search is stuck in these two stagnation states and then escape from them. Our proposed detect-and-escape strategy uses the feasible ratio and the change rate of overall constraint violation to detect stagnation, and adjusts the weight of the constraint violation for guiding the search to escape from stagnation states. We develop and implement a decomposition-based constrained MOEA with this strategy. Extensive experiments on a number of benchmark problems demonstrate the competitiveness of our proposed algorithm when compared to five other state-of-the-art constrained evolutionary algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a differential-evolution-based approach implementing a global search strategy to find a near-optimal axis-parallel decision tree is introduced. In this paper, the internal nodes of a decision tree are encoded in a real-valued chromosome, and a population of them evolves using the training accuracy of each one as its fitness value. The height of a complete binary decision tree whose number of internal nodes is not less than the number of attributes in the training set is used to compute the chromosome size, and a procedure to map a feasible axis-parallel decision tree from one chromosome is applied, which uses both the smallest-position-value rule and the training instances. The best decision tree in the final population is refined replacing some leaf nodes with sub-trees to improve its accuracy. The differential evolution algorithm has been successfully applied in conjunction with several supervised learning methods to solve numerous classification problems, due to it exhibiting a good tradeoff between its exploitation and exploration skills, and to the best of our knowledge, it has not been utilized to build axis-parallel decision trees. To obtain reliable estimates of the predictive performance of this approach and to compare its results with those achieved by other methods, a repeated stratified ten-fold cross-validation procedure is applied in the experimental study. A statistical analysis of these results suggests that our approach is better as a decision tree induction method as compared with other supervised learning methods. Also our results are comparable to those obtained with random forest and one multilayer-perceptron-based classifier.

Computer vision, which is used to detect and track a specific target in image sequences, has drawn great attention in recent years. The process of tracking can be formulated as a dynamic optimization problem that identifies the optimal position of the target in each image. Differential evolution (DE), who owns the advantages of simplicity, parallel computing, and self-adaptive search for global optimization, is envisioned as a promising algorithm to provide effective target tracking. In this paper, several improvements are made in DE for better adaptability in target tracking. Specifically, we introduce two inferior individuals into the mutation stage, which further enriches the diversity of the population and speeds up the offspring's evolution. We also proceed several image preprocessing and build an adaptive Gaussian mixture model of the target to deal with the complex tracking scenarios. Experimental results show that the designed tracking algorithm based on our improved DE demonstrates a higher tracking accuracy and faster tracking speed in several challenging tracking scenarios.

The main motivation of this paper is to discuss some theoretical details of opposite-center learning (OCL) and further validate its effectiveness for the optimization problems. In order to reveal the strong flexibility of its definition, two analytical solutions of opposite-center point are deduced for 1-D case. In order to reduce its computational complexity for higher dimensions, several termination criterion of iterative process are discussed thoughtfully and then a simple and efficient criterion is found when considering both the algorithm performance and computation cost. Moreover, a uniform evaluation approach to compute an evaluation function is proposed and then different opposition strategies can be compared easily by means of the mathematical expectation of these functions. To further verify its practical performance, OCL mechanism is embedded into differential evolution (DE) for population initialization and generation jumping and opposite-center DE is proposed. Simulation results demonstrate the strong exploitation ability of OCL. The obtained results also confirm a good tradeoff of solution accuracy and convergence speed in solving various function optimizations.

Differential evolution (DE) is a simple and effective evolutionary algorithm that can be used to solve various optimization problems. In general, the population of DE tends to fall into stagnation or premature convergence so that it is unable to converge to the global optimum. To solve this issue, this paper proposes a tracking mechanism (TM) to promote population convergence when the population falls into stagnation and a backtracking mechanism (BTM) to re-enhance the population diversity when the population traps into the state of premature convergence. More specifically, when the population falls into stagnation, the TM is triggered so that the individuals who fall into the stagnant situation will evolve toward the excellent individuals in the population to promote population convergence. When the population goes into the premature convergence status, the BTM is activated so that the premature individuals go back to one of the previous statuses so as to restore the population diversity. The TM and BTM work together as a general framework and they are embedded into six classic DEs and nine state-of-the-art DE variants. The experimental results on 30 CEC2014 test functions demonstrate that the TM and BTM are able to effectively overcome the issues of stagnation and premature convergence, respectively, and therefore, enhance the performance of the DE significantly. Moreover, the experimental results also verify that the TM works together with the BTM as a general framework is better than other similar general frameworks.

An improved path planning for mobile robots is proposed based on the hybrid multi-objective bare-bones particle swarm optimization with differential evolution. The mathematical model for robot path planning is firstly devised as a tri-objective optimization with three indices, i.e., the path length, the smoothness degree of a path, and the safety degree of a path. Then, a hybrid multi-objective bare bones particle swarm optimization is developed to generate feasible paths by combining infeasible paths blocked by obstacles with feasible paths via improved mutation strategies of differential evolution. In addition, a new Pareto domination with collision constraints is developed to select the personal best position of a particle according to the definition of the collision degree of a path. Simulation results confirm the effectiveness of our algorithm.

The demand for solving large-scale complex problems continues to grow. Many real-world problems are described by a large number of variables that interact with each other in a complex way. The dimensionality of the problem has a direct impact on the computational cost of the optimization. During the last two decades, differential evolution has been shown to be one of the most powerful optimizers for a wide range of optimization problems. In this paper, we investigate its appropriateness for large-scale problems. We propose a new variation of differential evolution that exhibits good results on difficult functions with a large numbers of variables. The proposed algorithm incorporates the following mechanisms: the use of three strategies, the extended range of values for self-adapted parameters F and CR, subpopulations, and the population size reduction. The algorithm was tested on the CEC 2013 benchmark suite for largescale optimization, and on two real-world problems from the CEC 2011 benchmark suite on real-world optimization. A comparative analysis was performed with recently proposed algorithms. The analysis shows the superior performance of our algorithm on most complex problems, described by overlapping and nonseparable functions.

Differential evolution (DE) is a competitive and reliable computing technique for continuous optimization. A diversity-based selection has been proved to be valid to improve the performance of DE. However, further study can be done. In this paper, we propose two versions of colony fitness, fitness with the consideration of diversity information. Selection based on the first version of the colony is embodied in DE/rand/1, a basic DE algorithm, while selection based on the second version is used in CoBiDE, a state-of the-art DE algorithm. Our experiments are based on the 2005 Congress on Evolutionary Computation and the 2014 Congress on Evolutionary Computation benchmark functions. Experimental results show that our modification on algorithms leads to significantly better solutions than before.

Differential evolution (DE) is powerful for global optimization problems and constantly improved. However, satisfactory solutions of some functions can be hardly obtained so far. According to the experimental data of many state-of-the-art DE algorithms from the literature and our pre-experiment, solutions for F12 among the 25 CEC 2005 benchmark functions have an outstanding large mean error to the optimal value, while solutions for F15, F21, and F23-F24 all fall into one or several values. It can be seen that, in the involved state-of-the-art DE algorithms, JADE obtains the best solutions for F15, while EDEV obtains the best solutions for F12. In this paper, we modify the two DE algorithms for the two functions, respectively. Experimental results show that our modifications leads to significant improvement on solutions. As a result, solutions for these two functions are improved to an unprecedented degree.

Embedding flexibility in engineering systems is a promising way to improve system performance under uncertainty. However, decision-makers are often unclear when and how to exercise flexibility after it has been embedded in a system design. This paper presents an evolutionary rule-based framework to address this challenge. The proposed framework extends the traditional flexibility management framework by automatically calibrating decision rules using an evolutionary algorithm. The calibrated decision rules can be used by decision-makers to adaptively adjust decisions in response to a changing environment. By incorporating a decision rule and evolutionary algorithm, the proposed framework not only helps generate flexible designs in the system design phase, but also provides intuitive guidance for decision-makers to manage flexibility in the system operation phase. To demonstrate its application, the proposed framework is applied to generate and optimize the decision rule of capacity expansion strategy for a waste-to-energy system. The results show that the flexible design that expands capacity based on the decision rule generated by our proposed framework outperforms the design with a fixed capacity expansion plan. These findings demonstrate that the proposed framework can effectively support flexibility management in terms of improving the system’s overall expected net present value.

The optimal wheel/rail adhesion of heavy-duty locomotives under traction must be determined given that suboptimal wheel/rail adhesion may result in low creep utilization, skidding, and idling. Here, we present an algorithm for the online identification of adhesion parameters. The algorithm is used for the online parameter estimation of the nonlinear wheel/rail adhesion model. The factors that influence the wheel/rail adhesion–slip ratio relationship are analyzed and described using Burckhardt’s nonlinear model. Then, an identification model is established to obtain the corresponding likelihood function within the framework of parameter identification based on maximum likelihood. Given the nonlinearity of the problem, a modified differential evolution algorithm is used for the parameter estimation of the identification model to obtain an algorithm for the online estimation of the nonlinear adhesion model. Finally, numerical simulation experiments are conducted under different conditions. Experimental results show that the proposed algorithm can address the nonlinearity of the model and the uncertainty of the rail surface environment.

Recently, emotion recognition using low-cost wearable sensors based on electroencephalogram and blood volume pulse has received much attention. Long short-term memory (LSTM) networks, a special type of recurrent neural networks, have been applied successfully to emotion classification. However, the performance of these sequence classifiers depends heavily on their hyperparameter values, and it is important to adopt an efficient method to ensure the optimal values. To address this problem, we propose a new framework to automatically optimize LSTM hyperparameters using differential evolution (DE). This is the first systematic study of hyperparameter optimization in the context of emotion classification. In this paper, we evaluate and compare the proposed framework with other state-of-the-art hyperparameter optimization methods (particle swarm optimization, simulated annealing, random search, and tree of Parzen estimators) using a new dataset collected from wearable sensors. Experimental results demonstrate that optimizing LSTM hyperparameters significantly improve the recognition rate of four-quadrant dimensional emotions with a 14% increase in accuracy. The best model based on the optimized LSTM classifier using the DE algorithm achieved 77.68% accuracy. The results also showed that evolutionary computation algorithms, particularly DE, are competitive for ensuring optimized LSTM hyperparameter values. Although DE algorithm is computationally expensive, it is less complex and offers higher diversity in finding optimal solutions.

In recent years, electric vehicle charging stations (EVCSs) have been deployed very fast, which offer a platform for the energy coordination between electric vehicles (EVs) and the electricity distribution system (DS). This paper presents an energy management method for the operation of EVCSs and DS while considering their interaction. In this method, we first use a quality-of-service constrained decision model for EVCSs, which is formulated based on the network calculus theory. Then, the DS aims to maximize its utility by scheduling charging plans for EVCSs. Afterwards, a game based on the supply function equilibrium (SFE) model is adopted to analyze the energy interaction, in which each EVCS is regarded as the leader, and the DS as the follower. In this way, an optimization problem with equilibrium constraints is established. Moreover, a hybrid optimization algorithm based on differential evolution and interior point method is used to obtain the equilibrium solution of the SFE game. Finally, simulation studies verify the effectiveness of proposed interactive energy management method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Bi-Magnet machine design concept in which ferrite and rare earth magnet materials are both utilized for torque production. The proposed design aims to reduce the cost of permanent magnet machines by minimizing the use of expensive high-grade rare earth magnet materials without compromising the high performance associated with them. The decreased performance due to the reduced rare earth magnet content can be compensated by adding inexpensive ferrite magnets. The design was optimized to achieve maximum cost-savings by minimizing the rare earth magnet content and improving the saliency of the rotor structure. A comparative study was conducted to highlight the advantages of the proposed concept using the Camry 2007 model as the baseline design. The results show that the proposed design is capable of achieving the same torque and power density as the baseline design using 30% less rare earth magnet materials, resulting in 20% active material cost reduction. The Bi-Magnet machine concept is shown to maintain the superior performance of conventional rare earth permanent magnet machines at a lower cost. [ABSTRACT FROM AUTHOR]

The joint base matrix ${{{{B}}_{{\mathrm{J}}}}}$ of the joint source-channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) codes consists of four components, namely, the source code ${{{{B}}_{{\mathrm{s}}}}}$ , the channel code ${{{{B}}_{{\mathrm{c}}}}}$ , the type-1 connection edge ${{{{B}}_{{ \textit {L1}}}}}$ and the type-2 connection edge ${{{{B}}_{{ \textit {L2}}}}}$ , each having a non-negligible influence on the system performance. Different from the traditional component-specific design approach, we propose a joint design and optimization algorithm based on the idea of multi-objective differential evolution (MODE). Specifically, we consider the optimization of the DP-LDPC JSCC system through joint design of three components ${{{{B}}_{{\mathrm{s}}}}}$ , ${{{{B}}_{{\mathrm{c}}}}}$ , ${{{{B}}_{{ \textit {L1}}}}}$ and all four components ${{{{B}}_{{\mathrm{s}}}}}$ , ${{{{B}}_{{\mathrm{c}}}}}$ , ${{{\mathbf{B}}_{{ \textit {L1}}}}}$ , ${{{{B}}_{{ \textit {L2}}}}}$ , respectively. The proposed algorithm has low search complexity due to the reduction in size and element value of base matrices. The joint protograph extrinsic information transfer (JPEXIT) analyses and the simulation results demonstrate that the resulting JSCC system is free from a high error floor, requires fewer number of iterations for reaching the same bit error rate (BER) and achieves significant coding gains as compared to the state-of-the-art. Our DP-LDPC JSCC system is also shown to outperform its separation-based counterpart by a wide margin. [ABSTRACT FROM AUTHOR]

An innovative architecture for sparsely excited tightly coupled dipole arrays is proposed, based on the irregular array techniques. In such a framework, an improved maximum-entropy model considering the difference of embedded element patterns is applied to optimize the irregular partition to suppress the grating lobes. Meanwhile, guidelines for the entire design procedure including antenna elements, power dividers, and optimization design of irregular feeding networks are presented. In order to achieve large elevation angle scanning, the differential evolution (DE) algorithm is used to synthesize active pattern and active reflection coefficient simultaneously. Finally, a 192-port prototype operating at 8–12 GHz has been fabricated and measured, where the port spacing is $0.576\lambda _{h}$ ($\lambda _{h}$ is the free-space wavelength at the highest frequency). Numerical and experimental results show that the prototype array is able to achieve the reduction of gain <1 dB for scanning to near broadside and about 2.7 dB for scanning up to 60°, in comparison with that of conventional Taylor arrays. [ABSTRACT FROM AUTHOR]

Robust control design for quantum systems has been recognized as a key task in quantum information technology, molecular chemistry, and atomic physics. In this paper, an improved differential evolution algorithm, referred to as multiple-samples and mixed-strategy DE (msMS_DE), is proposed to search robust fields for various quantum control problems. In msMS_DE, multiple samples are used for fitness evaluation and a mixed strategy is employed for the mutation operation. In particular, the msMS_DE algorithm is applied to the control problems of: 1) open inhomogeneous quantum ensembles and 2) the consensus goal of a quantum network with uncertainties. Numerical results are presented to demonstrate the excellent performance of the improved machine learning algorithm for these two classes of quantum robust control problems. Furthermore, msMS_DE is experimentally implemented on femtosecond (fs) laser control applications to optimize two-photon absorption and control fragmentation of the molecule CH2BrI. The experimental results demonstrate the excellent performance of msMS_DE in searching for effective fs laser pulses for various tasks. [ABSTRACT FROM AUTHOR]

To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity–concavity and connectedness–disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Locating more peaks and refining the solution accuracy on the found peaks are two challenging issues in solving multimodal optimization problems (MMOPs). To deal with these two challenges, a distributed individuals differential evolution (DIDE) algorithm is proposed in this article based on a distributed individuals for multiple peaks (DIMP) framework and two novel mechanisms. First, the DIMP framework provides sufficient diversity by letting each individual act as a distributed unit to track a peak. Based on the DIMP framework, each individual uses a virtual population controlled by an adaptive range adjustment strategy to explore the search space sufficiently for locating a peak and then gradually approach it. Second, the two novel mechanisms named lifetime mechanism and elite learning mechanism (ELM) cooperate with the DIMP framework. The lifetime mechanism is inspired by the natural phenomenon that every organism will gradually age and has a limited lifespan. When an individual runs out of its lifetime and also has good fitness, it is regarded as an elite solution and will be added to an archive. Then the individual restarts a new lifetime, so as to bring further diversity to locate more peaks. The ELM is proposed to refine the accuracy of those elite solutions in the archive, being efficient in dealing with the solution accuracy issue on the found peaks. The experimental results on 20 multimodal benchmark test functions show that the proposed DIDE algorithm has generally better or competitive performance compared with the state-of-the-art multimodal optimization algorithms. [ABSTRACT FROM AUTHOR]

The growing penetration of distributed energy sources (DES), such as photovoltaic (PV) solar power, battery energy systems and electric vehicles (EVs) into low voltage distribution networks is creating serious challenges for distribution network operators. Uncertain nature of these DES and EV charging is a key factor to cause unbalance, which degrade network performance in terms of energy loss, voltage unbalance, and voltage profile of the distribution network, etc. Some methods were proposed to mitigate such negative impact of these uncertain DES and EV charging from both centralized and decentralized approaches by controlling charging or discharging power of EVs. However, these methods involve all active EVs to participate in coordination and this causes significant inconvenience to EV owners along with requirements of complex communication infrastructure and huge data processing overhead. This article proposes an Internet of Things -based centralized control strategy to coordinate EV and DES distribution by using the differential evolution (DE) optimization algorithm. The obtained results show that the proposed control strategy can improve network performance (voltage imbalance, neutral current, energy loss, and node voltage) significantly. In addition, the control strategy is less demanding on communication infrastructure and convenient for EV owners as well as having a lighter data processing overhead. [ABSTRACT FROM AUTHOR]

Owing to environmental concerns, the energy-efficient subway train scheduling problem is necessary in subway operation management. This paper designs an approximate dynamic programming (DP) approach for energy-efficient subway train scheduling problem with time-dependent demand. The train traffic model is proposed with the dynamic equations for the evolution of train headway, train passenger loads, and the energy consumption along the subway line. For the dynamic changing of the onboard passengers with time, the total train energy usage is modeled as the sum of energy consumptions from the traction system and auxiliary facilities. A nonlinear DP problem is formulated to generate a near optimal timetable to realize the tradeoff among the utilization of trains, passenger waiting time, service levels, and energy consumption. To overcome the curse of dimensionality in this optimization problem, we construct an approximate DP framework, where the conceptions of states, policies, state transitions, and reward function are introduced. And this algorithm is able to converge to a good solution with a short time compared to the genetic algorithm and differential evolution algorithm. Finally, the numerical experiments are given to demonstrate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targeting DBPs from primary sequences. In TargetDBP, four single-view features, i.e., AAC (Amino Acid Composition), PsePSSM (Pseudo Position-Specific Scoring Matrix), PsePRSA (Pseudo Predicted Relative Solvent Accessibility), and PsePPDBS (Pseudo Predicted Probabilities of DNA-Binding Sites), are first extracted to represent different base features, respectively. Second, differential evolution algorithm is employed to learn the weights of four base features. Using the learned weights, we weightedly combine these base features to form the original super feature. An excellent subset of the super feature is then selected by using a suitable feature selection algorithm SVM-REF+CBR (Support Vector Machine Recursive Feature Elimination with Correlation Bias Reduction). Finally, the prediction model is learned via using support vector machine on the selected feature subset. We also construct a new gold-standard and non-redundant benchmark dataset from PDB database to evaluate and compare the proposed TargetDBP with other existing predictors. On this new dataset, TargetDBP can achieve higher performance than other state-of-the-art predictors. The TargetDBP web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/targetdbp/ for academic use. [ABSTRACT FROM AUTHOR]

This article presents a differential evolution algorithm with a new encoding mechanism and Cauchy mutation (DE-NEM-CM) for optimizing large unequally spaced planar array layouts with the minimum element spacing constraint. In the new encoding mechanism, each individual represents a certain element position rather than an entire array layout used in traditional stochastic optimization algorithms. Such an encoding mechanism has the following advantages: 1) in each individual updating, the array pattern can be efficiently evaluated by only considering the radiation contribution variation from one element movement, which can greatly reduce the computational time; 2) it naturally facilitates the generated new array layout in population updating to meet the minimum element spacing constraint, and 3) each individual is searched always in 2-D space as the array size increases. These advantages enable it to be very suitable for synthesizing large arrays. Besides, DE serves as a search engine, and Cauchy mutation with chaotic mapping is proposed to enhance the local search while preserving the diversity of the population. A set of experiments for synthesizing different types of unequally spaced planar arrays in both narrow-and broadband applications are conducted. Synthesis results show that the proposed method achieves much lower sidelobe level than some state-of-the-art stochastic optimization methods for all the test cases. Importantly, the proposed method is much more efficient than conventional stochastic optimization algorithm especially for the case of synthesizing large unequally spaced planar array layouts. A array layout optimization with more than 1000 elements can be achieved within acceptable CPU time cost, which has not yet been reported for the existing stochastic optimization methods without resorting to supercomputing facilities. [ABSTRACT FROM AUTHOR]

This paper presents a control strategy for the position and posture control of a planar four-link underactuated manipulator with a passive second link based on a back-propagation neural network (BPNN) model. First, using the differential evolution (DE) optimization algorithm, the target angles of all links are calculated. The third and fourth links are then controlled from their initial angles to zero. Next, the control process is divided into the following two steps: the control objective of the passive link is implemented in the first step, and the control objectives of the active links are implemented in the second step. In the first step, the system is reduced to a planar virtual Pendubot, a BPNN model based on the DE algorithm is trained to obtain the coupling relationship between the links of this Pendubot. This model gives the speed of the first link to move the passive link to approach the target angle. Then, a fuzzy controller is designed to indirectly control the passive link to its target angle. In the second step, the position mode of the servo controller is adopted to control all active links to their target angles. Experimental results verified the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Various mutation strategies show distinct advantages in differential evolution (DE). The cooperation of multiple strategies in the evolutionary process may be effective. This article presents an underestimation-assisted global and local cooperative DE to simultaneously enhance the effectiveness and efficiency. In the proposed algorithm, two phases, namely, the global exploration and the local exploitation, are performed in each generation. In the global phase, a set of trial vectors is produced for each target individual by employing multiple strategies with strong exploration capability. Afterward, an adaptive underestimation model with an self-adapted slope control parameter is proposed to evaluate these trial vectors, the best of which is selected as the candidate. In the local phase, the better-based strategies guided by individuals that are better than the target individual are designed. For each individual accepted in the global phase, multiple trial vectors are generated by using these strategies and filtered by the underestimation value. The cooperation between the global and local phases includes two aspects. First, both of them concentrate on generating better individuals for the next generation. Second, the global phase aims to locate promising regions quickly while the local phase serves as a local search for enhancing convergence. Moreover, a simple mechanism is designed to determine the parameter of DE adaptively in the searching process. Finally, the proposed approach is applied to predict the protein 3-D structure. The experimental studies on classical benchmark functions, CEC test sets, and protein structure prediction problem show that the proposed approach is superior to the competitors. [ABSTRACT FROM AUTHOR]

Artificial intelligence evolved as a powerful tool in condition monitoring of the induction motor for early diagnosis of bearing faults. This paper attempted to identify the features to improve the accuracy of diagnosis using the benchmark database of the Case Western Reserve University (CWRU) and the Machinery Fault Prevention Technology (MFPT). In this paper, 18 time-domain features are extracted constituting six proposed time-dependent spectral features (TDSFs) and 12 statistical time-domain features. For the first time, a set of six TDSFs are extracted to diagnose the bearing faults. This involves extracting frequency-domain features using the relationship between Parseval’s theorem and the Fourier transform directly from the time domain. Particle swarm optimization (PSO) and wheel-based differential evolution (WBDE) feature selection algorithms are also implemented to identify four prominent features. It is found that three of the four selected features are TDSF features, and results of selected features revealed the attainment of 90.92% for 48 class identifications of CWRU database and 100% for 17 class identifications of the MFPT database, respectively. [ABSTRACT FROM AUTHOR]

A numerical method for providing linear phase response of bulk acoustic wave (BAW) piezoelectric transducers (PTs) of acousto-optic devices is developed. Our approach is based on the analytic approximation of the phase response of the PT with the matching electrical circuit and optimization with a differential evolution genetic algorithm. Simulations and experiments were performed for two typical BAW excitation schemes: direct and reflective. Variance of the group delay in the specified frequency range was reduced by an order of magnitude for reflective excitation of BAWs. [ABSTRACT FROM AUTHOR]

The practical NAND flash memory suffers from various non-stationary noises that are difficult to be predicted. For example, the data retention noise induced channel offset is unknown during the readback process, and hence severely affects the reliability of data recovery from the memory cell. In this paper, we first propose a novel recurrent neural network (RNN)-based detector to effectively detect the data stored in the multi-level-cell (MLC) flash memory without the prior knowledge of the channel. However, compared with the conventional threshold detector, the proposed RNN detector introduces much longer read latency and more power consumption. To tackle this problem, we further propose an RNN-aided (RNNA) dynamic threshold detector, whose detection thresholds can be derived based on the outputs of the RNN detector. We thus only need to activate the RNN detector periodically when the system is idle. Moreover, to enable soft-decision decoding of error-correction codes, we first show how to obtain more read thresholds based on the hard-decision read thresholds derived from the RNN detector. We then propose integer-based reliability mappings based on the designed read thresholds, which can generate the soft information of the channel. Finally, we propose to apply density evolution (DE) combined with the differential evolution algorithm to optimize the read thresholds for low-density parity-check (LDPC) coded flash memory channels. Computer simulation results demonstrate the effectiveness of our proposed RNNA dynamic read thresholds design, for both the uncoded and LDPC-coded flash memory channels, without any prior knowledge of the channel. [ABSTRACT FROM AUTHOR]

The use of industrial robots has grown over the years, making production systems increasingly efficient. Within this context, some limitations appear that can delay the productive process causing damages to the production. These limitations are robot stops, for example. Stops can be caused by various factors, such as accidents, collisions of manipulator robots with operators or other equipment. The main contribution of this research is to improve the Artificial Potential Field (APF) using Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) by optimizing the APF parameters in collision avoidance. We present as results: the trajectories generated by a planar manipulator robot; the position errors between the final position and the last position of the generated trajectories; and the computational cost of the PSO, GA and DE algorithms to find the parameters of the APF algorithm. [ABSTRACT FROM AUTHOR]

The steelmaking process in the iron and steel industry involves complicated physicochemical reactions. The main aim of steelmaking is to adjust the quality of molten steel. During the steel-tapping process, the temperature and carbon content are the most essential quality indices for end-point prediction. This article presents a novel machine learning framework for the end-point prediction problems in the smelting process. Considering the importance of data representation in modeling, the original data are inputted to a stacked autoencoder (SAE) to extract the essential features in an unsupervised manner. The top layer is then designed as a sparse Bayesian regression (SBR) layer to obtain the predicted mean values and error bars that measure the uncertainty in the prediction. To improve the generalization ability of the prediction model, an intelligent optimization algorithm based on improved differential evolution (DE) is used to optimize the hyperparameters of the model. The main advantage of this model is that the underlying characteristics of the samples can be learned automatically layer by layer, instead of designing them manually. Finally, the effectiveness of the proposed method is verified using real data collected from two steel plants. The experimental results show that the proposed model gives a more precise prediction than other existing models and can provide error bars for the end-point prediction. Note to Practitioners—The end-point prediction is critical to the quality of products produced in the iron and steel production process. Because of the complexity of the physiochemical reactions during smelting, it is difficult to build mechanism models for the complex environment. Generally, expert knowledge and experience are needed for real production. The motivation behind this article was to establish a data-driven model through machine learning methods to solve end-point prediction problems. First, a stacked autoencoder (SAE) was used to extract the essential information from the original data. Second, sparse Bayesian regression (SBR) was applied to the top layer. Thus, the predicted mean values and error bars could be obtained to improve the robustness of the prediction model. Furthermore, an improved differential evolution (DE) algorithm was designed to adaptively optimize the hyperparameters of the model. In the experiments, the proposed method was applied to two real steelmaking production processes. The results verify the effectiveness of the proposed model. This article can be extended to other processes such as continuous casting and reheating furnace. The model can also be generalized for other practical industries to improve the product quality and enable safer production. [ABSTRACT FROM AUTHOR]

Nonlinear equations system (NES) arises commonly in science and engineering. Repulsion techniques are considered to be the effective methods to locate different roots of NES. In general, the repulsive radius needs to be given by the user before the run. However, its optimal parameter setting is difficult and problem-dependent. To alleviate this drawback, in this paper, we first propose a dynamic repulsion technique, and then a general framework based on the dynamic repulsion technique and evolutionary algorithms (EAs) is presented to effectively solve NES. The major advantages of our framework are: 1) the repulsive radius is controlled dynamically during the evolutionary process; 2) multiple roots of NES can be simultaneously located in a single run; 3) the diversity of the population is preserved due to the population reinitialization; and 4) different repulsion techniques and different EAs can be readily integrated into this framework. To extensively evaluate the performance of our framework, we choose 42 problems with diverse features as the test suite. In addition, some representative differential evolution and particle swarm optimization variants are incorporated into the framework. Our method is also compared with other state-of-the-art methods. Experimental results indicate that the dynamic repulsion technique can improve the performance of the original repulsion technique with static repulsive radius. Moreover, the proposed method is able to yield better results compared with other methods. [ABSTRACT FROM AUTHOR]

Finding multiple roots of nonlinear equation systems (NESs) in a single run is one of the most important challenges in numerical computation. We tackle this challenging task by combining the strengths of the repulsion technique, diversity preservation mechanism, and adaptive parameter control. First, the repulsion technique motivates the population to find new roots by repulsing the regions surrounding the previously found roots. However, to find as many roots as possible, algorithm designers need to address a key issue: how to maintain the diversity of the population. To this end, the diversity preservation mechanism is integrated into our approach, which consists of the neighborhood mutation and the crowding selection. In addition, we further improve the performance by incorporating the adaptive parameter control. The purpose is to enhance the search ability and remedy the trial-and-error tuning of the parameters of differential evolution (DE) for different problems. By assembling the above three aspects together, we propose a repulsion-based adaptive DE, called RADE, for finding multiple roots of NESs in a single run. To evaluate the performance of RADE, 30 NESs with diverse features are chosen from the literature as the test suite. Experimental results reveal that RADE is able to find multiple roots simultaneously in a single run on all the test problems. Moreover, RADE is capable of providing better results than the compared methods in terms of both root rate and success rate. [ABSTRACT FROM AUTHOR]

The grey wolf optimizer (GWO) is a newly developed swarm intelligence-based optimization technique that mimics the social hierarchy and group hunting behavior of grey wolves in nature. Here, a detailed introduction of the GWO algorithm is given, after which, three sets of examples are investigated: first, numerical experiments on four benchmark functions are conducted; second, the GWO is applied to the synthesis of linear arrays with the aim of reducing the peak sidelobe level under various constraints; and finally, the performance of the GWO is further verified on the optimization design of two representative antennas, namely, a dual-band E-shaped patch antenna and a wideband magneto-electric dipole antenna. The results show that the GWO is capable of outperforming or providing very competitive results compared with some well-known metaheuristics such as the genetic algorithm, particle swarm optimization, and differential evolution. Thus, it may serve as a promising candidate for handling electromagnetic problems. [ABSTRACT FROM AUTHOR]

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is an efficient mathematical strategy for solving multiobjective optimization problems. However, the MOEA/D algorithm has not yet been widely used on the multiobjective optimal power flow (MOPF) problems, which consider several conflicting objectives with varying tradeoff levels. This article proposes a novel differential evolution (DE) strategy based on the MOEA/D framework to quickly determine a set of optimal solutions of MOPFs, in the objective space formed from the different objectives, such as the most optimal economic dispatch, the least environmental emission objectives, and the minimum transmission losses, while considering the power system constraints. A judicious decision can be made by the user from the set of optimal solutions of the MOPF associated with the weight vectors representing the tradeoff levels of the different objectives. For improved performance, two aggregate objective functions, a load flow operator and a self-adaptive DE strategy work cooperatively: 1) to improve the weak convergence of the MOEA/D and to achieve a better decision speed; 2) to obtain more accurate optimal solutions even under non-convex conditions; 3) to ensure that the power system constraints are taken into account; 4) to integrate the above features into a fast and efficient algorithm. The proposed algorithm has been validated using the IEEE 30-bus system and a revised 33-bus radial system added to one node of the 30-bus system. The simulation results show that the proposed algorithm can provide a good accuracy and can converge to a set of optimal solutions of the MOPFs. [ABSTRACT FROM AUTHOR]

Coupling matrix (CM) synthesis methods for all-resonator diplexers and multiplexers are far from mature. For complex coupling topologies, the existing methods are often not able to find the appropriate CMs that satisfy the S-parameter specifications. To address this challenge, a new synthesis method, which hybridizes analytical and optimization techniques, called general all-resonator diplexer/multiplexer CM synthesis (GACMS) method, is proposed in this article. The two main innovations of GACMS are: 1) an optimization framework incorporating filter design knowledge, which effectively reduces the search space for CM synthesis and 2) a new memetic algorithm-based optimizer, which tackles the challenges from the complex landscape (function characteristics) of CM synthesis problems. GACMS is tested by six complex practical problems and CMs are successfully obtained for all of them. Comparisons with the existing methods demonstrate the advantages of GACMS in terms of solution quality and robustness. [ABSTRACT FROM AUTHOR]