Hybrid Differential Evolution Algorithm Research Articles

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.

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.

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 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.

Flood disaster evaluation based on adaptive fuzzy clustering iterative model and hybrid differential evolution algorithm

Abstract In order to reasonably and rapidly evaluate flood disaster, based on a fuzzy clustering iterative model (FCI) and differential evolution algorithm (DE), an adaptive fuzzy clustering iterative model using a hybrid differential evolution algorithm (AFCI-HDE) is proposed, which has three advantages: firstly, the decision-maker's subjective preference was considered to flexibly modify the objective function; secondly, HDE was introduced to optimize the index weight vector of AFCI; thirdly, the validity of its clustering effect was more credible than that of FCI. Finally, the case study revealed that AFCI-HDE is feasible and effective by comparing the optimal fitness and clustering validity values with other approaches, which could reflect various decision-maker's preferences by simple adaptive adjustments and rapidly obtain reasonable evaluation results, thus providing a new effective approach in flood risk management.

Kalman filter and multi-stage learning-based hybrid differential evolution algorithm with particle swarm for a two-stage flow shops scheduling problem

Inspired by the advantages of hybrid intelligent optimization methods, this paper at first proposes a hybrid differential evolution with particle swarm optimization (DEPS) to solve a two-stage hybrid flow shops scheduling problem. On the basis of analyzing the convergence and optimization scheme of DEPS, the Kalman filter algorithm and a multi-stage learning strategy are then creatively fused into DEPS, namely KLDEPS, to enhance the running performance of the algorithm. The introduction of the Kalman filter enriches the diversity of individuals and enhances the neighborhood search ability of the algorithm, and the combination with the multi-stage learning strategy has beneficial effect on jumping out of the local optimal scheme. To make the proposed KLDEPS more suitable for a real manufacturing environment, the constraints of queueing time between two stages, different job sizes and processing time are imposed on the scheduling problem. The performance of the proposed KLDEPS is evaluated by comparing with two other high-performing intelligent optimization algorithms. The computational results reveal that the proposed KLDEPS outperforms the other two algorithms both in solutions’ quality and convergence rate.

A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation

This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation.

Hybrid differential evolution algorithms for the optimal camera placement problem

PurposeThis paper aims to investigate to what extent hybrid differential evolution (DE) algorithms can be successful in solving the optimal camera placement problem.Design/methodology/approachThis problem is stated as a unicost set covering problem (USCP) and 18 problem instances are defined according to practical operational needs. Three methods are selected from the literature to solve these instances: a CPLEX solver, greedy algorithm and row weighting local search (RWLS). Then, it is proposed to hybridize these algorithms with two hybrid DE approaches designed for combinatorial optimization problems. The first one is a set-based approach (DEset) from the literature. The second one is a new similarity-based approach (DEsim) that takes advantage of the geometric characteristics of a camera to find better solutions.FindingsThe experimental study highlights that RWLS and DEsim-CPLEX are the best proposed algorithms. Both easily outperform CPLEX, and it turns out that RWLS performs better on one class of problem instances, whereas DEsim-CPLEX performs better on another class, depending on the minimal resolution needed in practice.Originality/valueUp to now, the efficiency of RWLS and the DEset approach has been investigated only for a few problems. Thus, the first contribution is to apply these methods for the first time in the context of camera placement. Moreover, new hybrid DE algorithms are proposed to solve the optimal camera placement problem when stated as a USCP. The second main contribution is the design of the DEsim approach that uses the distance between camera locations to fully benefit from the DE mutation scheme.

Inferring oncoenzymes in a genome-scale metabolic network for hepatocytes using bilevel optimization framework

Abstract Cancer cells exhibit unusual metabolic activity, characterized by high rates of glucose consumption and lactate production, even under aerobic conditions, as well as increased glutamine catabolism and amino acid metabolism. Based on the behaviors of metabolic reprogramming, we can infer oncogenesis from a genome-scale model of cancer cell metabolism. This study establishes a bilevel optimization formulation that integrates the genome-scale metabolic model of hepatocytes, the Warburg hypothesis, and LC/MS experimental data to detect multiple-hit enzyme deficiencies that induce metabolic reprogramming in hepatocytes. A nested hybrid differential evolution algorithm was employed to solve the bilevel optimization problem. The results predicted dopa decarboxylase (DDC) to be an influential enzyme that causes oncogenesis. A cluster analysis of the flux variations for different enzyme deficiencies obtained through a flux variability analysis showed that DDC is a dominant overexpressed enzyme, and it was classified into a group with similar trends of flux and metabolite alternations.

A hybrid differential evolution algorithm for mixed-variable optimization problems

Mixed-variable optimization problems (MVOPs) that involve continuous and discrete decision variables widely exist in industrial and scientific domains. However, how to solve MVOPs efficiently remains an open issue because the fact that continuous and discrete variables present different spatial distribution features posts a great challenge to algorithmic design. In this paper, a hybrid differential evolution (DE) framework is proposed for MVOPs. The proposed framework, namely DEMV, hybridizes the original DE and the set-based DE for evolving continuous and discrete variables, respectively. The two DEs are selected for hybridization because algorithmic analysis and experimental studies show that they share the same search mechanism. The compatibility and consistency of the two DEs is the key for enabling DEMV to coevolve different types of decision variables efficiently. Experiments are conducted on a set of artificial MVOPs converted from continuous benchmark functions and real-world engineering problems with mixed variables. Experimental results and comparisons with other representative algorithms show that DEMV is effective and efficient.

Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for the Sugarcane Cultivation Scheduling Problem

Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for the Sugarcane Cultivation Scheduling Problem

Multi-objective Hybrid DE Algorithm for Solving VRPTW

For the characteristics of the Vehicle Routing Problem with Time Windows(VRPTW) ô€€€a multi-objective hybrid Differential Evolution algorithm for VRPTW is proposed. Firstly, through a linearly varying parameter controls the probability of choice of DE/rand/1 mutation strategy and DE/best/1 mutation strategy. Secondly, a crossover operation based on merge sort is designed. Finally, selection operations employ Pareto-dominated concepts and ring rules to rank individuals and output non-dominated solutions. The experimental results compared with single strategy DE algorithm and ABC algorithms show that the proposed algorithm is effective in solving the VRPTW.

3D flow simulation of straight groynes using hybrid DE-based artificial intelligence methods

In this study, hybrid differential evolution algorithms were run to study the three-dimensional mean flow field around straight groynes. The three-dimensional velocity components in a refined mesh around a groyne were measured using an acoustic Doppler velocimeter. Two novel hybrid methods, namely differential evolution-based multilayer perceptron (DE-MLP) and differential evolution-based radial basis function (DE-RBF), were used to simulate the most significant flow features. It was found that the DE-MLP method modeled well the separation of the bottom boundary layer, downward deflection of the upper layers’ streamlines and horseshoe vortex (HSV) development at the upstream groyne face. However, the DE-RBF model was unable to predict the main flow features correctly, especially the HSV system. Both DE-MLP and DE-RBF models predicted the overall flow structure of the separation zone downstream of the groyne well. However, the RBF model could not predict the inflection points in the transverse velocity profiles, which are indicative of a mixing layer bounding the separation zone. The DE-RBF model underestimated the velocity amplification at the groyne head. The DE-MLP model perfectly simulated the distribution of the streamwise cross-stream Reynolds shear stress around the groyne head and also along the turbulent mixing layer.

Optimal power flow using hybrid differential evolution and harmony search algorithm

In this paper, Optimal Power Flow (OPF) with non-convex and non-smooth generator fuel cost characteristics is proposed using Hybrid Differential Evolution and Harmony Search (Hybrid DE-HS) algorithm. The proposed OPF formulation includes active and reactive power constraints; prohibited zones, and valve point loading effects of generators. In the problem formulation, transformer tap settings and reactive power compensating devices settings are also considered as the control variables. Therefore, OPF is a complicated optimization problem, hence there is a need to solve this problem with an accurate algorithm. The OPF solution is obtained by considering generator fuel cost, transmission loss and voltage stability index as objective functions. The effectiveness of the proposed hybrid algorithm is validated on IEEE 30, 118 and 300 bus test systems, and the results obtained with proposed Hybrid DE-HS algorithm are compared with other optimization techniques reported in the literature. For example, the quadratic fuel cost obtained by the hybrid DE-HS algorithm for IEEE 30 bus system is 799.0514 $/h, saving 0.31% of the cost obtained by the General Algebraic Modeling System (GAMS) software. This benefit increases further with the size of the system.

Hybrid Differential Evolution Algorithm Employed for the Optimum Design of a High-Speed PMSM Used for EV Propulsion

This paper presents a new optimization approach, based on a hybrid algorithm, used for the design of a new high-speed permanent magnet synchronous machine (HS-PMSM). The HS-PMSM is used for the electromagnetic propulsion system (i.e., the machine will be connected to a magnetic gear) of an electric vehicle. The hybrid optimization algorithm combines two differential evolution methods and a memory mechanism, in order to increase the quality of the algorithm. The evolution of the rotor structure of the HS-PMSM (running at 22 000 r/min) is presented, as well as some aspects of the mechanical risks due to centrifugal forces. The best suited variant will be optimized based on the new optimization approach and the solution will be numerically and experimentally validated.

An adaptive hybrid differential evolutionary algorithm for the parameter identification of rotating machinery

An adaptive hybrid differential evolution algorithm for the dynamic parameter identification of rotating machinery is developed in this paper. Genetic algorithm (GA) is first used to reduce the range of the identification parameters to save computational time. Adaptive Cauchy and Gaussian mutations are then applied in a differential evolution algorithm (DEA) and dynamically updated during the evolutionary process according to the identification process, to obtain the global optimal solution more easily and more rapidly. Numerical studies of linear and nonlinear rotor-bearing systems are performed. The results indicate that the genetic algorithm-adaptive hybrid differential evolution algorithm (GA-AHDE) is more effective at identifying the dynamic parameters and faults of the rotor-bearing system compared to other evolutionary algorithms.

Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm

Objective: In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem. Method: In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions. Results: The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.

Hybrid Differential Evolution and Greedy Algorithm (DEGR) for solving Multi-Skill Resource-Constrained Project Scheduling Problem

Paper presents a hybrid Differential Evolution and Greedy Algorithm (DEGR) applied to solve Multi-Skill Resource-Constrained Project Scheduling Problem. The specialized indirect representation and transformation of solution space from discrete (typical for this problem), to continuous (typical for DE-approaches) are proposed and examined. Furthermore, Taguchi Design of Experiments method has been used to adjust parameters for investigated method to reduce the procedure of experiments. Finally, various initialisation, clone elimination, mutation and crossover operators have been applied there. The results have been compared with the results from other reference methods (HantCO, GRASP and multiStart Greedy) using the benchmark iMOPSE dataset. This comparison shows that DEGR effort is very robust and effective. For 28 instances of iMOPSE dataset DEGR has achieved the best-known solutions.

Fuzzy portfolio selection model with real features and different decision behaviors

In the ever changing financial markets, investor’s decision behaviors may change from time to time. In this paper, we consider the effect of investor’s different decision behaviors on portfolio selection in fuzzy environment. We present a possibilistic mean-semivariance model for fuzzy portfolio selection by considering some real investment features including proportional transaction cost, fixed transaction cost, cardinality constraint, investment threshold constraints, decision dependency constraints and minimum transaction lots. To describe investor’s different decision behaviors, we characterize the return rates on securities by LR fuzzy numbers with different shape parameters in the left- and right-hand reference functions. Then, we design a novel hybrid differential evolution algorithm to solve the proposed model. Finally, we provide a numerical example to illustrate the application of our model and the effectiveness of the designed algorithm.