Hybrid Differential Evolution Research Articles

Energy Management Micro Grid using Hybrid De Algorithm with Genetic Algorithm

his paper evaluates hybrid differential evolution algorithm and genetic algorithm of LAMG is used to solving the medium scale mixed integer programming problems. Hybrid GA and DE algorithm is implemented in Local place Micro grids. LPMG and the required need of power and choose more power plants with power production with the help of Genetic algorithm. Genetic algorithm can be introduced with local place micro grid and select any one of the power plant. In this DEA implemented the local place MG, then survey period is one day. Last calculation shows which time or hour produce more power and sold out power in nearest city area electricity board. DE algorithm determine the one day power survey and Genetic algorithm choose more hour and select any one of the hour for better power production. The hybrid DEA and GA is to maintain choosing and selecting of better power production. So our project aim is choose more hour and select any one of the hour for better power production. This hybrid is use DE and GA is to maintain the real and reactive power of any power plant

Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering

In this paper, the Gaussian mixture model (GMM) is introduced to the channel multipath clustering. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters. However, the EM widely converges into local optimization. To address this issue, a hybrid differential evolution (DE) and EM (DE-EM) algorithms are proposed in this paper. To be specific, the DE is employed to initialize the GMM parameters. Then, the parameters are estimated with the EM algorithm. Thanks to the global searching ability of DE, the proposed hybrid DE-EM algorithm is more likely to obtain the global optimization. Simulations demonstrate that our proposed DE-EM clustering algorithm can significantly improve the clustering performance.

Design and implementation of sharp edge FIR filters using hybrid differential evolution particle swarm optimization

The filter is an important building block of modern communication and electronic systems. Based on well-defined bandwidth, it extracts the desired portion of the spectrum when the raw input signal is applied. Efficacy of filtering depends on the preciseness of bandwidth to avoid co-channel interference and signal loss. In addition, it must provide higher stopband attenuation and passband attenuation very close to unity with a tolerable quantity of pass/stop band ripple. Design/implementation of sharp edge modern FIR filter is structured as a multi-objective, constrained, complex, and highly nonlinear (hence multimodal) error minimization challenge. Hence, this work proposes a novel objective (normalized error fitness) function and a robust hybrid algorithm for the effective searching of the optimal filter coefficients for providing excellent sharp edge frequency response during the filtering action. Most popular particle swarm optimization (PSO) and differential evolution (DE) algorithm are effectively combined together to frame the proposed hybrid DE-PSO algorithm for enhancing the exploration and exploitation abilities of it. The proposed hybrid algorithm is validated using twelve different benchmark functions. Through simulations, the qualitative performance of the proposed approach is compared with the conventional PSO, DE, real-coded genetic algorithm and the Parks–McClellan method.

Multiobjective Design Optimization of Generalized Multilayer Multiphase AC Winding

The need for higher efficiency motors is becoming more relevant due to recent policies adopted in most advanced economies, which are adopting new minimum energy performance standards. A great effort to optimize several parts of rotating electric machines is being done to fulfill that requirement. Recent studies focus on stator winding optimization, because of its high loss share. In this paper, a general multilayer winding configuration is proposed for winding design optimization for multiphase symmetrical machines, by combining multilayer integer-slot, fractional-slot, and fractional-slot concentrated windings. The constrained minimization of the airgap magnetomotive force harmonic distortion and winding resistance is solved by the hybridization of differential evolution with a non-dominating sorting algorithm. A framework is provided for replication purposes. An experimental application example of standard and optimized windings is included for performance assessment. The optimized winding leads to a significant motor efficiency gain, while reducing the amount of copper needed for manufacturing.

Mitigation of power oscillations using hybrid DE-PSO optimization-based SSSC damping controller

This paper presents an optimal design of a static synchronous series compensator (SSSC)-based controller for damping of low-frequency oscillations in multi-machine power systems. The proposed controller design problem is formulated to the optimization problem. The tuning of controller parameters can be obtained by employing a new hybrid differential evolution and particle swarm optimization (hDE-PSO) algorithm. To justify the effectiveness of the proposed SSSC-based damping controller, three-machine and four-machine power systems have been considered. The hDE-PSO algorithm outperforms in the damping of oscillations over DE and PSO algorithms. Various simulation results are presented and compared for different load disturbances like three-phase fault, load rejection and tripping of one parallel transmission line. The simulation results ensure the robustness of the proposed controller.

Hybrid differential evolution with the topographical heuristic

Hybrid differential evolution with the topographical heuristic

Hybrid differential evolution with the topographical heuristic

Hybrid differential evolution with the topographical heuristic

A Hybrid Firefly-Swarm Optimized Fractional Order Interval Type-2 Fuzzy PID-PSS for Transient Stability Improvement

This article focuses on the implementation of a hybrid firefly algorithm-particle swarm optimization (FAPSO) scheme for optimizing the parameters of an interval type-2 fractional order fuzzy proportional integral derivative (IT2FOFPID)-based power system stabilizer (PSS) to minimize the low-frequency oscillations in a power system. Here, the IT2FOFPID-based PSS is designed by considering speed deviation and acceleration as input signals. In this article, a single machine infinite bus system and the New England 10 machine 39-bus power system are used for testing and comparing the approaches. Stability studies are also performed using OPAL-RT's OP5600, a real-time digital simulator. The comparative studies demonstrate that the hybrid FAPSO optimized IT2FOFPID-PSS provides better damping and stability performance when compared with the PSSs based on the FA/PSO/ hybrid genetic algorithm and bacterial foraging optimization and hybrid differential evolution and pattern search optimized IT2FOFPID approaches under various operating scenarios.

Designing of cost minimum substation grounding grid system using DE, SCA, and HDESCA techniques

This study presents three optimisation techniques to obtain optimum design of grounding grid with equal and unequal spacing between grid conductors. Different algorithms such as differential evolution (DE), sine–cosine algorithm (SCA) and hybrid DE SCA (HDESCA) are used in the design, and its efficacy are analysed. Aim of the present work is not only to limit the value of safety parameters, also to achieve the optimum grounding system by reducing total cost involved in designing of the grid. Here, necessary safety and security conditions as mentioned in ANSI/IEEE Standard 80–2000 is adopted to design the grounding grid. The cost function used here includes grid depth, dimension of grounding rod, conductor, number of rods and conductors, revetment and excavation area. The economic grounding grid system has been obtained to reduce the total cost related with grid materials, installation, and excavation. The effectiveness of proposed method and its results are theoretically examined with recently reported techniques that ascertain the validity and efficiency. Parameters obtained by DE, SCA, and HDESCA are compared to design the equal and unequal spaced grounding grid. The design based on HDESCA technique gives less cost and minimum safety parameters than those obtained by DE and SCA.

A Sustainable Multimodal Transport System: The Two-Echelon Location-Routing Problem with Consolidation in the Euro–China Expressway

Sustainable development of transport systems is a common topic of concern and effort in multiple countries, in which reducing carbon emissions is one of the core goals. Multimodal transport is an effective way to achieve carbon emission reduction and to efficiently utilize transport resources. The intercontinental transport system, represented by the Euro–China Expressway, is a prominent exploration that has recently received attention, which promotes the sustainable development of transport between countries and carbon emission reduction. In the intercontinental multimodal transport system, the reasonable connection of roads and railways, especially the optimization of consolidation, is an important link which affects the system’s carbon emissions. This paper focuses on the consolidation of sustainable multimodal transport and summarizes the multimodal transport two-echelon location-routing problem with consolidation (MT-2E-LRP-C). We aim to solve multimodal consolidation optimization problem, especially locations of multimodal station, by routing of highway and railway. We propose a two-layer mixed integer linear problem (MILP) model, which highlights the consolidation of roads and railways, focuses on road and rail transport connections, and optimizes road routes and railway schemes. To validate the MT-2E-LRP-C model, we design a series of random instances for different quantities of nodes. In order to solve large-scale instances and realistic transport problems, we propose a hybrid differential evolution algorithm, which decomposes the problem into a railway layer and a highway layer for heuristic algorithm solving. Furthermore, the MILP model and algorithm are tested by small-scale random instances, and the hybrid differential evolution algorithm is solved for the large-scale random instances. Finally, we solve the realist instance from the Euro–China Expressway to develop instructive conclusions.

An integrated vendor–buyer replenishment policy for deteriorating items with fuzzy environment and resource constraint

Different from previous researches, our study considers a perishable item with collaborative vendor–buyer ordering policy and finite replenishment rate. Furthermore, due to the importance of inventory and capital investment in today’s fuzzy marketing environments, researches in fuzzy collaborative inventory models have become very popular research in recent decades. Therefore, in our integrated model with deteriorating inventory replenishment policy, we construct the crisp/fuzzy models with inventory investment constraints with fuzzy environments. Two different fuzzy decision-making methods are used to formulate the models. Convex fuzzy programming (CFP) method is used to maximize the weighted sum for each achievement level of the joint cost and constraints. An inverse weighted fuzzy non-linear programming (IWFNLP) is then proposed to satisfy the decision-maker’s desirable achievement level of service. A heuristic algorithm with mixed-integer hybrid differential evolution (MIHDE) is developed to solve the crisp/fuzzy models. Numerical examples and sensitivity analysis are developed to investigate the effectiveness of the proposed method in the fuzzy environment. From the numerical analysis, it can be seen that the IWFNLP method is a more efficient decision-making tool than the CFP method.

Hybridization DE with K-means for speaker clustering in speaker diarization of broadcasts news

In this paper, we address the problem of optimal non-hierarchical clustering in the speaker clustering phase for the speaker diarization task of news broadcasts. A new hybridization combining differential evolution (DE) algorithm and K-means algorithm is proposed and tested on TV news database (TVND). To optimize the classification of speakers, two criteria, namely trace within criterion (TRW) and variance ratio criterion (VRC), were used as clustering validity indices, correcting every possible grouping of speakers’ segments. Concerning the encoding of the classification of clusters to be optimized, it is performed by the cluster centers in DE algorithm. Therefore, a problem of rearrangement of centers in the populations can be generated, which cannot ensure an efficient search by applying evolutionary operators. For this purpose, an efficient heuristic was also proposed for this rearrangement. Non-hybrid DE variants were applied with and without the rearrangement of cluster centers, and compared with the corresponding hybrid K-means variants. The experimental results have showed the high-efficiency of hybrid K-means variants with the rearrangement of cluster centers compared with those without the rearrangement of cluster centers and non-hybrid DE variants. Also, the obtained results using hybrid and non-hybrid DE variants with the rearrangement of cluster centers were quite similar using both TWR and VRC criteria. Moreover, the best efficiency was acquired using hybrid DE variants thanks to these two criteria from which a value of 13.05% of DER has been reached by hybrid b6e6rl variant.

Selective maintenance process optimization based on an improved gravitational search algorithm, from the perspective of energy consumption

Maintenance is indispensable for ensuring that multi-component systems successfully complete the next mission. Maintenance activities require an enormous amount of energy consumption in engineering problems. For selective maintenance, many studies focus on the trade-off between system reliability and other resources, such as time and cost. The enormous energy consumption of maintenance processes has been ignored in previous research. In this work, a novel energy consumption model considering the actual maintenance process is proposed. Moreover, system reliability is added to the optimization model as a crucial constraint, which makes the optimization process of energy consumption meaningful. The selective maintenance process is a complex combinatorial optimization problem. Hence, a hybrid differential evolution and gravitational search algorithm (DGSA) is proposed to solve the selective maintenance problem considering energy consumption. In addition, several experiments with DGSA, particle swarm optimization and differential evolution are designed to demonstrate the effectiveness of the proposed algorithm.

A New Hybrid Differential Evolution with Gradient Search for Level Set Topology Optimization

Topology optimization is an effective structural optimization concept for optimal design of engineering structures. However, it has many difficulties due to high number of design variables and complex problems same as compliant mechanisms and crashworthiness. Conventional methods for topology optimization does not have enough adaptability with current computer aided design (CAD) softwares and they are not powerful in solving difficult optimization problems. Level set which is a novel boundary tracking method had been recently used to solve problems in conventional methods. This paper is dedicated to propose a new hybrid method based on differential evolution (DE) and globally convergent method of moving asymptotes (GCMMA) to use both gradient direction of GCMMA and excellent exploration of DE. The method has been validated in familiar benchmark problems in compliance minimization.

An Intelligent Battery Energy Storage-Based Controller for Power Quality Improvement in Microgrids

Modern power systems rely on renewable energy sources and distributed generation systems more than ever before; the combination of those two along with advanced energy storage systems contributed widely to the development of microgrids (MGs). One of the significant technical challenges in MG applications is to improve the power quality of the system subjected to unknown disturbances. Hence innovative control strategies are vital to cope with the problem. In this paper, an innovative online intelligent energy storage-based controller is proposed to improve the power quality of a MG system; in particular, voltage and frequency regulation at steady state conditions are targeted. The MG system under consideration in this paper consists of two distributed generators, a diesel synchronous generator, and a photovoltaic power system integrated with a battery energy storage system. The proposed control approach is based on hybrid differential evolution optimization (DEO) and artificial neural networks (ANNs). The controller parameters have been optimized under several operating conditions. The obtained input and output patterns are consequently used to train the ANNs in order to perform an online tuning for the controller parameters. Finally, the proposed DEO-ANN methodology has been evaluated under random disturbances, and its performance is compared with a benchmark controller.

A Hybrid of Differential Evolution and Genetic Algorithm for the Multiple Geographical Feature Label Placement Problem

Label placement is a difficult problem in automated map production. Many methods have been proposed to automatically place labels for various types of maps. While the methods are designed to automatically and effectively generate labels for the point, line and area features, less attention has been paid to the problem of jointly labeling all the different types of geographical features. In this paper, we refer to the labeling of all the graphic features as the multiple geographical feature label placement (MGFLP) problem. In the MGFLP problem, the overlapping and occlusion among labels and corresponding features produces poorly arranged labels, and results in a low-quality map. To solve the problem, a hybrid algorithm combining discrete differential evolution and the genetic algorithm (DDEGA) is proposed to search for an optimized placement that resolves the MGFLP problem. The quality of the proposed solution was evaluated using a weighted metric regarding a number of cartographical rules. Experiments were carried out to validate the performance of the proposed method in a set of cartographic tasks. The resulting label placement demonstrates the feasibility and the effectiveness of our method.

A hybrid differential evolution algorithm with column generation for resource constrained job scheduling

Resource constrained job scheduling problems are ubiquitous in real-world logistics and supply chain management. By solving these optimisation problems, organisations can efficiently utilise logistical resources and improve delivery performance. Because of their complexity, finding optimal solution is challenging. Existing solution methods based on integer programming and meta-heuristics have shown promising results for small instances but become less efficient when they are applied to large-scale instances with hundreds of jobs. This paper presents a new hybrid optimisation method that combines the power of differential evolution, iterated greedy search, mixed integer programming, and parallel computing to solve resource constrained job scheduling problems. The experimental results with existing benchmark datasets and a set of 1755 newly generated instances show that the proposed algorithm can find high quality solutions even for hard instances. For small and medium instances, the optimality gaps of the proposed algorithms are significantly better than those of the mixed integer programming solver and the column generation algorithm. For large instances, the proposed algorithms can find solutions with significantly better upper bounds as compared to existing meta-heuristics and the state-of-the-art hybrid algorithm. The analyses also confirm the advantage of using multiple processing cores to improve the efficiency and solution quality of the proposed algorithm.

Total flow time minimization in no-wait job shop using a hybrid discrete group search optimizer

The no-wait job shop scheduling problem is a well-known NP-hard problem and it is typically decomposed into timetabling subproblem and sequencing subproblem. By adopting favorable features of the group search technique, a hybrid discrete group search optimizer is proposed for finding high quality schedules in the no-wait job shops with the total flow time criterion. In order to find more promising sequences, the producer operator is designed as a destruction and construction (DC) procedure and an insertion-based local search, the scrounger operator is implemented by differential evolution scheme, and the ranger operator is designed by hybridizing best insert moves. An efficient initialization scheme based on Nawaz–Enscore–Ham (NEH) heuristic is designed to construct the initial population with both quality and diversity. A speed-up method is developed to accelerate the evaluation of the insertion neighborhood. Computational results based on well-known benchmark instances show that the proposed algorithm clearly outperforms a hybrid differential evolution algorithm and an iterated greedy algorithm. In addition, the proposed algorithm is comparable to a local search method based on optimal job insertion, especially for large-size instances.

Security Constrained Optimal Reactive Power Dispatch Using Hybrid Particle Swarm Optimization and Differential Evolution

The optimal operation of a power system in both normal and contingency cases has a significant role in the power system operation. To guarantee a system to operate securely in both cases, the most severed case should be included in the optimal reactive power dispatch (ORPD) problem. The objective of the SCORPD problem is to solve the ORPD problem in both normal and contingency cases so that the total power loss, stability index, or voltage deviation is the system is minimized satisfying all unit and network constraints. In this article, a hybrid particle swarm optimization and differential evolution (HPSO-DE) is proposed to solve this SCORPD problem. The proposed method is a combination of PSO and DE methods to utilize their advantages so that the search ability of the method can be enhanced. The proposed method has been implemented on the IEEE 30-bus system for different objectives with different scenarios. The obtained results have been indicated that the proposed HPSO-DE method can be very effective for dealing with the complex and large-scale SCORPD problem

Hybrid IWD-DE: A Novel Approach to Model Cooperative Navigation Planning for Multi-robot in Unknown Dynamic Environment

The current study emphasizes inadequacies of earlier research findings on multi-robot navigation and offers a robust approach to guide the robots in an unknown dynamic workspace. To attain the objective, the Intelligent Water Drop (IWD) algorithm is combined with Differential Evolution (DE) in a new conceptual fashion to optimize the navigation path of multiple mobile robots. The hybrid IWD-DE, basic IWD and DE algorithms are then explored for multi-robot navigation in a dynamic workspace through simulation. The simulation outcomes demonstrate the competency of the proposed method to guide the robot in predicting the nature of the obstacle and to generate an optimal and safe trajectory in a dynamic workspace. The potency of the proposed algorithm is further validated through experimentation with fire bird robot in a real platform. The outcomes of the simulation and experimentation, by employing IWD-DE algorithm are compared with those of basic IWD and DE algorithm. The error estimated for Average Navigation Path Travelled (ANPT), Aggregate Navigation Path Deviation (ANPD) and number of turns between simulation and experimental results by employing IWD-DE is 8.96, 9.09, and 11.13, respectively while reaching 16.43, 28.61, 29.15 for IWD, and 6.66, 10.20, 14.28 for DE. The efficiency of the proposed approach is further verified with the state-of-the-art in a dynamic workspace. The outcomes reveal the efficiency and flexibility of the proposed approach in resolving the multi-robot navigation problem as compared to the state-of-the-art in a dynamic workspace.