Hybrid Differential Evolution Algorithm Research Articles

Fast multi-type resource allocation in local-edge-cloud computing for energy-efficient service provision

With the advancement of information technology, the concept of local-edge-cloud computing has gained prominence. Operating on a collaborative model, heterogeneous computing nodes converge to deliver a spectrum of multi-type services, including calculation-intensive, latency-sensitive, and privacy-requiring services. This collaborative approach fosters high-quality development in power economy. However, the proliferation of heterogeneous computing nodes, while beneficial, introduces challenges. The intricate connections and limited energy supply may lead to interruptions in the service processes of nodes. In this study, we present an energy-efficient resource allocation scheme designed for low-latency multi-type service provision within a local-edge-cloud collaboration. Our methodology focuses on optimizing the performance of multi-type service provision in a local-edge-cloud network, taking into account considerations such as latency, resource allocation, and energy consumption. To accomplish this, we employ the Alopex-based Differential Evolution algorithm. Initially, we construct three sub-models to analyze latency and energy aspects across various computing modes. Subsequently, we formulate a constrained optimization problem aimed at minimizing both latency and energy consumption in multi-type service provisioning. These models seek to derive optimal resource allocation decisions for the given scenario. To address this optimization problem, we introduce the hybrid differential evolution algorithm, Alopex-DE. A formal analysis is conducted to showcase its near-optimal performance in comparison to three state-of-the-art algorithms. Additionally, extensive simulations are carried out to validate the superior effectiveness of our proposed approach.

Enhancing production inventory management for imperfect items using fuzzy optimization strategies and Differential Evolution (DE) algorithms

In the complex landscape of production inventory management for manufacturing industries, this research brings to light the significant impact of imperfect items—often an overlooked aspect that can result in elevated costs and operational inefficiencies. Addressing this oversight, we introduce a tailored mathematical model that harmoniously integrates demand forecasting, production planning, quality checks, and inventory control measures, specifically when imperfect items are in play. Notably, our model leverages the power of hybrid intelligent algorithms, fusing fuzzy systems with both genetic and differential evolution (DE) algorithms. Through sensitivity analysis, we discern the intricate effects of varying parameters on system performance. To cater to different operational exigencies, we introduce two distinct decision-making approaches. Our results highlight a promising reduction in anticipated yearly expenses, bolstering the reliability of events and ensuring costs are kept within predefined financial thresholds. Furthermore, we unveil a pioneering hybrid DE algorithm that distinctly outperforms the foundational DE in optimization problem-solving. A side-by-side comparison of our introduced methods elucidates invaluable insights into inventory cash flow management. Practitioners stand to gain immensely from our findings, which pave the way for cost-effective strategies, superior quality control, and an uptick in overall operational efficiency in the face of imperfect items. This seminal work not only offers actionable strategies but also beckons future research opportunities, thereby pushing the frontiers of manufacturing systems.

Integrated optimization of production scheduling and maintenance planning with dynamic job arrivals and mold constraints

Effective management of production often requires harmonizing maintenance and production activities. However, decsions on production scheduling and machine maintenance are usually made independently, leading to potential overlaps and inefficiencies. This paper joinly investigates the production scheduling and maintenance planning for a production machine subject to different types of jobs that arrive randomly. A unique emphasis is placed on the prerequisite of loading specific molds prior to job execution, an element often overlooked in previous studies. Maintenance considerations employ the reliability/availability framework. The overarching goal is the creation of an integrated schedule that minimizes the weighted sum of total costs and the machine's maximum unavailability. To address this intricate challenge, a novel hybrid differential evolution and genetic algorithm (DE-GA) is proposed, complemented by a solution refinement strategy. Performance evaluations indicate that DE-GA methodology consistently outperforms Gurobi solver and four other prevalent algorithms across different test instances.

Hybrid differential evolution algorithm for the resource constrained project scheduling problem with a flexible project structure and consumption and production of resources

The resource constrained project scheduling problem with a flexible project structure and consumption and production of resources, involves making a selection of activities and scheduling these activities in order to minimize the makespan, subject to precedence and resource constraints. Since finding a feasible selection of activities is NP-hard, we introduce the concept of group graphs and restrict ourselves to instances with an acyclic group graph. For these instances, which represent many practical cases, we show how to make a feasible selection of activities in polynomial time and use this concept to schedule the selected activities using a hybrid differential evolution algorithm. We compare this algorithm with an algorithm from the literature on special cases of instances without consumption and production of resources, and show that our algorithm creates solutions of higher quality. Furthermore, to compare general instances, we develop an ant colony optimization algorithm that performs slightly better on special cases than the algorithm from literature and show that the hybrid differential evolution algorithm outperforms the ant colony optimization algorithm on general instances.

Research on Green Vehicle Path Planning of AGVs with Simultaneous Pickup and Delivery in Intelligent Workshop

In this study, we present and discuss a variant of the classic vehicle routing problem (VRP), the green automated guided vehicle (AGV) routing problem, which involves simultaneous pickup and delivery with time windows (GVRPSPDTW) in an intelligent workshop. The research object is AGV energy consumption. First, we conduct a comprehensive analysis of the mechanical forces present during AGV transportation and evaluate the overall operational efficiency of the workshop. Then, we construct a green vehicle path planning model to minimize the energy consumption during AGV transportation and the standby period. Hence, the problems considered in this study are modeled based on asymmetry, making the problem solving more complex. We also design a hybrid differential evolution algorithm based on large neighborhood search (DE-LNS) to increase the local search ability of the algorithm. To enhance the optimal quality of solutions, we design an adaptive scaling factor and use the squirrel migration operator to optimize the population. Last, extensive computational experiments, which are generated from the VRPSPDTW instances set and a real case of an intelligent workshop, are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results show that DE-LNS yields competitive results, compared to advanced heuristic algorithms. The effectiveness and applicability of the proposed algorithm are verified. Additionally, the proposed model demonstrates significant energy-saving potential in workshop logistics. It can optimize energy consumption by 15.3% compared with the traditional VRPSPDTW model. Consequently, the model proposed in this research carries substantial implications for minimizing total energy consumption costs and exhibits promising prospects for real-world application in intelligent workshops.

Design of Novel Ultrabroadband Printed Antenna and Its Efficient Optimization Using Self-Adaptive Hybrid Differential Evolution Algorithm

A novel compact ultra-broadband-modified fork-shaped printed antenna is optimized efficiently by using a self-adaptive hybrid differential evolution (SHDE) algorithm. Firstly, a novel compact ultra-broadband-modified fork-shaped printed antenna structure is proposed. The antenna with more compact size consists of a modified fork-shaped radiator and a modified ground plane, which can cover a very wide operating frequency band. The antenna is fed by a step-shaped microstrip line, and the modified ground plane consists of some rectangular slots and an L-shaped stub. Then, the SHDE algorithm is used to determine structural dimensions of the proposed antenna, and the antenna’s performance is optimized while maintaining a cost-effective computation time. The optimized antenna with only 11.8 mm × 19.7 mm size covers -10 dB reflection coefficient bandwidth of 147.6% from 3.08 to 20.46 GHz. Finally, the antenna prototype is fabricated, and the measured results basically agree with the simulated ones. The proposed antenna can be viewed as an excellent candidate for realizing ultrabroadband transmission technology.

Robust Multi-objective optimal dispatching model for a novel island micro energy grid incorporating biomass waste energy conversion system, desalination and power-to-hydrogen devices

To realize renewable and self-sustainable energy supply in island region, based on geographical characteristics with abundant renewable resources, an optimal model for island micro energy grid (MEG) is designed incorporating biomass waste energy conversion system (ECS), desalination, and power-to-hydrogen (BSP-MEG) Firstly, the mathematical model is designed, including models of power generators, ECS, desalination and power-to-hydrogen (P2H) devices, etc. Next, the multi-objective scheduling optimization model is designed, containing conventional scheduling model (Scheduling optimization objectives and constraints established with minimum operation and environment costs) and stochastic scheduling model (Minimum Conditional Value-at-Risk objective specific to volatility and uncertainty of renewable generations based on robust stochastic optimization method). Then, to solve the multi-objective optimization problem (MOP), a hybrid differential evolution algorithm is proposed based on local optimal and external archiving strategies. Finally, the MEG of YongXing Island is selected as an example. The results show (1) BSP-MEG effectively realized multi-energy cooperative optimization, and promote intra-day peak shaving. (2) BSP-MEG reduced operating costs, environmental costs and Conditional Value-at-Risk (CVaR) by 78.2%, 61.8% and 77.9% respectively, while curtailment rate by 25.6 to 0.9%. (3) Whether in general scenario or worst, BSP-MEG can realize self-production and self-sale of energy and material, of which risk resistance ability is better. (4) By designing local optimal and external archiving strategies, hybrid differential evolution algorithm performs better in solving complex MOP. In general, the optimization model proposed in this paper can improve the utilization of renewable resources, alleviate the shortage of fresh water, and help realize renewable and sustainable energy supply.

Developing a novel approach for missing data imputation of solar radiation: A hybrid differential evolution algorithm based eXtreme gradient boosting model

Having sufficient and qualified datasets is of paramount importance in terms of understanding the internal dynamics of the nature-related phenomenon. Given the necessity to maintain the completeness of the datasets, this study introduced a novel technique containing the implementation of machine learning algorithms and a meta-heuristic optimization algorithm for imputing the gaps encountered in measurements of solar radiation which is one of the crucial meteorological variables in terms of not only climate dynamics but also energy technologies. To accomplish this aim, four different gap sizes, i.e., 5 %, 10 %, 20 %, and 30 %, have synthetically been constituted and the applicability of the extreme gradient boosting (XGBoost) configured by the differential evolution (DE) was examined for each gap size. The corresponding model was benchmarked with conventional interpolation techniques (i.e., linear and spline optimizations) and other widely applied ML algorithms (i.e., random forest and multivariate adaptive regression splines). A multi-perspective input selection strategy was considered to model the missing values based on correlation coefficients under three scenarios encompassing a total of 14 different models. The results revealed that the XGBoost-DE model generated with the solar radiation measurements of neighboring stations was found as the best-performed model in all gap sizes, i.e., 5 % (NSE: 0.950; KGE: 0.967), 10 % (NSE:0.934; KGE: 0.962), and 30 % (NSE: 0.939; KGE: 0.957), but 20 % which the highest accuracy was obtained with the RF (NSE: 0.944; KGE: 0.966). On the other hand, the interpolation techniques had the lowest accuracies among their counterparts in imputation attempts with respect to all gap size alternatives.

An Improved Chaos Driven Hybrid Differential Evolutionand Butterfly Optimization Algorithm for Passive Target Localization Using TDOA Measurements

This paper addresses the problem of time difference of arrival (TDOA) based passive target localizationand proposes an improved chaos-driven hybrid differential evolution (DE) algorithm and butterfly optimization algorithm (BOA), named ICDEBOA, to solve this complex optimization problem. The proposed algorithm consists of a new mutation strategy with the mechanisms of the BOA algorithm incorporated into the DE algorithm. To boost optimization effectiveness, chaos theory is employed to adjust the control parameter values. The considered localization problem is formulated using the maximum likelihood estimator. To perform the accuracy comparison, the convex constrained weighting least squares algorithm is applied to the considered localization problem as the widely used method in literature. The statistical analysis shows that the proposed modifications to the ICDEBOA algorithm improve its optimization performance, as demonstrated by the improved performance on the CEC2014 benchmark problems. The ICDEBOA algorithm is also shown to be more robust than existing algorithms in noisy environments. Numerical simulation results show that the proposed ICDEBOA algorithm meets the CRLB and achieves better performance than the CWLS, DE, and BOA algorithms.

A hybrid differential evolution algorithm for a location-inventory problem in a closed-loop supply chain with product recovery

Product recovery is an important business because of its great economic, social, and environmental benefits in practice. In this paper, a location-inventory problem (LIP) in a closed-loop supply chain (CLSC) is investigated to optimize facility location and inventory control decisions by considering product recovery. The objective is to optimize facility location and inventory control decisions to minimize the total cost of business operations in a closed-loop supply chain system. We formulate this problem as a mixed-integer nonlinear programming model and design a modified hybrid differential evolution algorithm (MHDE) to solve it efficiently. Finally, numerical results are presented to validate the performance of the new algorithm. The results show that MHDE is more efficient and effective than Lingo and other algorithms for the research problem under study. Managerial insights are also derived for business managers to improve their supply chain performance.

A discrete hybrid algorithm based on Differential Evolution and Cuckoo Search for optimizing the layout of ship pipe route

Based on Differential Evolution and Cuckoo Search, a hybrid optimization algorithm, called DDECS, is proposed in this paper for solving the ship pipe route layout optimization problem. The purpose of DDECS algorithm is to quickly and accurately optimize the ship pipe route under various kinds of constraints. Firstly, this paper proposed a vector coding method for ship pipe routes by studying the existing coding methods and ship pipe routes’ attributes. The proposed vector coding method has a concise structure, strong compatibility and is independent of the gridding space. And then, the Discrete Hybrid Differential Evolution and Cuckoo Search (DDECS) algorithm was implemented by using the proposed vector coding. The DDECS algorithm has few parameters and high computational efficiency, and it can effectively find the global optimal solution by maintaining the diversity of the population. Finally, three comparative simulations were presented to validate the effectiveness and engineering practicality of DDECS algorithm. The results showed that, the proposed DDECS algorithm can get an optimization scheme with fewer elbows and more remaining areas, which is significant for the layout optimization of the ship pipe route.

A hybrid differential evolution algorithm for a stochastic location-inventory-delivery problem with joint replenishment

A practical stochastic location-inventory-delivery problem with multi-item joint replenishment is studied. Unlike the conventional location-inventory model with a continuous-review (r, Q) inventory policy, the periodic-review inventory policy is adopted with multi-item joint replenishment under stochastic demand, and the coordinated delivery cost is considered. The proposed model considers the integrated optimization of strategic, tactical, and operational decisions by simultaneously determining (a) the number and location of distribution centers (DCs) to be opened, (b) the assignment of retailers to DCs, (c) the frequency and cycle interval of replenishment and delivery, and (d) the safety stock level for each item. An intelligent algorithm based on particle swarm optimization (PSO) and adaptive differential evolution (ADE) is proposed to address this complex problem. Numerical experiments verified the effectiveness of the proposed two-stage PSO-ADE algorithm. A sensitivity analysis is presented to reveal interesting insights that can guide managers in making reasonable decisions.

A hybrid differential evolution algorithm for flexible job shop scheduling with outsourcing operations and job priority constraints

Owing to the increasing complexity of products and the specialization of enterprises, production outsourcing has become a common practice in industrial manufacturing. Moreover, different jobs feature various priorities in actual production. The previous research that aims to minimize the makespan may not be applicable in real scenarios. Therefore, this study investigates a flexible job shop scheduling problem with outsourcing operations and job priority constraints. We propose a sequence-based mathematical model aiming at minimizing weighted overdue days, which considers the outsourcing constraints and different overdue weights of jobs with different priorities. An efficient hybrid self-adaptive differential evolution algorithm with heuristic strategies (HSDE) is proposed to address this problem. In HSDE, a well-designed chromosome encoding and decoding method is presented. To eliminate individuals that do not satisfy the outsourcing constraints, we add a penalty term to improve the objective function. By considering heuristic strategies for initial chromosome generation, the proposed approach is able to achieve a high-quality initial population. Crossover and mutation operators with self-adaptive control of the parameters are established to enlarge the search range and accelerate the convergence speed. Finally, several experiments are conducted to verify the effectiveness of the proposed model and algorithm. Experimental results confirm that the proposed algorithm outperforms other algorithms both in efficiency and accuracy.

Discrete-time rate-dependent hysteresis modeling and parameter identification of piezoelectric actuators

A discrete-time-modified Bouc–Wen model is proposed to describe the non-symmetrical and rate-dependent hysteresis of piezoelectric actuators for micro-vibration control applications. The modified model combines a non-symmetrical Bouc–Wen model and a frequency-dependent dynamic module. A series of experiments are conducted to characterize the rate-dependent hysteresis of piezoelectric stack actuators under sinusoidal excitations at a range of 1 to 20 Hz. The experimental results verify the validity of the modified model. The modified Bouc–Wen model increases the complexity of Bouc–Wen hysteresis nonlinear differential equation, which brings difficulties to parameter identification. To identify the parameters of Bouc–Wen model, an improved hybrid differential evolution and Jaya (DE-Jaya) algorithm is introduced with a hybrid mutant operator and Jaya operator that tried to balance between convergence speed and solution accuracy. The improved algorithm is tested on benchmark functions and compared with other optimizations to prove its effectiveness. The comparison results show that hybrid DE-Jaya algorithm has better performance in convergence speed and solution accuracy. The identified discrete-time-modified Bouc–Wen model is used as the secondary path in a filtered-x variable step-size affine projection algorithm (FXVSSAPA), and experimental verifications are done on a micro-vibration control platform. The experimental results show that the FXVSSAPA algorithm can converge to the steady-state error faster and verify the effectiveness of the proposed discrete-time-modified Bouc–Wen model.

Hybrid Differential Evolution and Krill Herd Algorithm for the Optimal Design of Water Distribution Networks

For optimally designing water distribution networks (WDNs), the nondeterministic polynomial hard problem, a novel hybrid model, is introduced with the combined features of evolutionary and swarm intelligence techniques. An evolutionary algorithm with better exploration properties, differential evolution (DE), and the swarm intelligence technique with better exploitation properties, namely the krill herd algorithm (KHA), is considered for this purpose. Because exploration and exploitation are the essential features of the metaheuristic algorithms, the hybrid algorithm with a combination of the DE and KHA features, the DE-KHA, resulted in a balanced search methodology. The results on the application of the proposed model on well-studied benchmark problems have demonstrated its enhanced search behavior, converging faster to the promising results with considerable robustness. Moreover, compared with other competing algorithms reported for optimally designing the WDNs, the DE-KHA outperforms with better computational efficiency. Additionally, considering the few control parameters that have to be calibrated for their optimal values, the computational burden will be less for performing the sensitivity analysis. As a result, considering the solution precision, quick convergence ability, and robustness of DE-KHA, the study suggests the algorithm for efficiently handling real-life case studies.

Fuzzy multiobjective hierarchical optimization with application to identify antienzymes of colon cancer cells

BackgroundDeveloping a new drug from conception to launch is costly and time consuming. Computer-aided methods can reduce research cost and accelerate the development process during the early drug discovery and development stages. MethodsThe present study proposed a fuzzy hierarchical optimization framework for identifying potential anticancer targets in genome-scale metabolic models. In this framework used for evaluating treatment side effects, the mortality of treated cancer cells, viability and metabolic deviation of perturbed normal cells were assigned fuzzy objective functions. Fuzzy set theory and the nested hybrid differential evolution (NHDE) algorithm were applied to solve a maximizing decision-making problem transformed from the hierarchical optimization problem. We verified the existence and limitation of this transformation. Significant FindingsThe NHDE algorithm in the optimization framework identified 12 one-target genes with high hierarchical fitness scores by using a small population size. The experimental data accessed from the DepMap database indicated that most of the identified targets can cause most cell lines to die (except EBP, LSS, and NSDHL). Two-target combinations resulted in higher cell viability and lower side effects than did one-target treatments. However, three- and four-target combinations did not significantly improve cell viability and reduce side effects relative to the two-target combinations.

Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer.

Simple SummaryDiscovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.

An intelligent way for optimal controller placements in software-defined–IoT networks for smart cities

Software Defined Networks (SDNs) is modern network paradigm that separately manages the control logic-plane and hardware data-plane. The programmable network SDN centrally manages different smart devices connected in the networks. These smart devices are facilitated by Internet of Things (IoT) and collect real time data for network devices. One of the emerging applications of IoT network is Smart City which purpose is to manage the city without human interventions. The rapid growths of IoT devices like actuators and sensors in smart city are generating large amount of data and increasing the traffic in network. To maintain the quality of services (QoS) of smart city IoT networks, the SDN characteristic is one way to provide better services to network users. Traditional IoT network having the challenges like, access delays, security and reliability issues. The SDN enabled IoT networks are capable to overcome these challenges and improves the network performance. One single controller is not sufficient to manage all IoT devices in smart city networks. So, multiple controllers are required to handle Software Defined-IoT Networks (SDN-IoT). Different metaheuristic algorithms are implemented to overcome controller placements problems (CPP) but as the number of controllers increases the performance of approaches degrades due to premature conversions. To better balance the exploration and exploitation features an intelligent Hybrid Differential Evolution and Whale Optimization (DEWO) Algorithm is proposed that optimizes the controller placements in SDN-IoT enabled smart city networks. The algorithm searches the optimal locations of controllers and balance the switch loads in respect of minimum latency. The algorithm also minimizes the link failure and evaluate the minimum end to end delay. The proposed hybrid algorithm is evaluated up to 40 controllers with other state of art method and able to achieve better results in comparison with other metaheuristic algorithms.

Array Pattern Synthesis Using a Hybrid Differential Evolution and Analytic Algorithm

In this paper, a hybrid differential evolution and weight total least squares method (HDE-WTLSM) is proposed for antenna array pattern synthesis. A variable diagonal weight matrix is introduced in total least squares method. Then, the weight matrix is optimized by differential evolution (DE) algorithm to control the differences of the desired level and the obtained level in different directions. This algorithm combines the advantages of evolutionary algorithm and numerical algorithm, so it has a wider application range and faster convergence speed. To compare HDE-WTLSM with DE algorithm and typical numerical algorithms, these methods are applied to a linear antenna array and a conformal truncated conical array. Using our method, lower sidelobe levels and deeper nulls are obtained. The simulation results verify the validity and efficiently of HDE-WTLSM.

A hybrid differential evolution and simulated annealing algorithm for global optimization

Differential evolution (DE) is one of the most effective ways to solve global optimization problems. However, considering the traditional DE has lower search efficiency and easily traps into local optimum, a novel DE variant named hybrid DE and simulated annealing (SA) algorithm for global optimization (HDESA) is proposed in this paper. This algorithm introduces the concept of “ranking” into the mutation operation of DE and adds the idea of SA to the selection operation. The former is to improve the exploitation ability and increase the search efficiency, and the latter is to enhance the exploration ability and prevent the algorithm from trapping into the local optimal state. Therefore, a better balance can be achieved. The experimental results and analysis have shown its better or at least equivalent performance on the exploitation and exploration capability for a set of 24 benchmark functions. It is simple but efficient.