Hybrid Evolutionary Algorithm Research Articles

An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions.

Ships’ response strategies to port disruptions caused by hurricanes

This paper studies ships' operational proactive strategies during the entire process of port disruptions due to environmental factors such as hurricanes. For the first time, the incompleteness and uncertainty of information, and the characteristics of port disruptions are considered. The problem is modeled as a nonhomogeneous Markov decision process (MDP), where different operational actions, such as sailing speed adjustment, handling rate adjustment, and port sequence adjustment are considered. The rolling horizon decision-making approach and a PSO-GA-based hybrid evolutionary algorithm are combined to solve the model. The results show that compared to the strategy without planned schedule adjustments, cost-saving up to 78.34% can be achieved by simultaneously adjusting the sailing speed, cargo handling rate, and port sequence. Counter-intuitively, we find that observing more complete hurricane information or having accurate forecasts does not always conducive to better decision-making. We also find the integration of multiple response strategies yields synergistic benefits. These findings will deepen our understanding of the impact of port disruptions and provide theoretical support for the decision-making of ship's response strategies.

A hybrid steady-state evolutionary algorithm using random swaps for Gaussian model-based clustering

A new hybrid evolutionary algorithm (EA) for Gaussian mixture model-based clustering is proposed. The EA is a steady-state method that, in each generation, selects two individuals from a population, creates two offspring using either mutation or crossover, and fine-tunes the offspring using the expectation maximization (EM) algorithm. The offspring compete with their parents for survival into the next generation. The approach proposed uses a random swap, which replaces a component mean with a randomly chosen feature vector as a mutation operator. In the crossover operator, a random component is copied from the source mixture into a destination mixture. Copying in crossover favors components of the source mixture located away from the components of the destination mixture. In computational experiments, the approach was compared to a multiple restarts EM algorithm, a random swap EM method, and a state-of-the-art hybrid evolutionary algorithm for Gaussian mixture model learning on one real and 29 synthetic datasets. The results indicate that, given the same computational budget, the proposed method usually learns mixtures with higher log-likelihoods than other benchmarks competing algorithms. The partitions of data obtained by the method correspond best to the original divisions of datasets into classes.

Multi-Objective Optimal Power Flow including Wind and Solar Generation Uncertainty Using New Hybrid Evolutionary Algorithm with Efficient Constraint Handling Method

A new hybrid decomposition-based multiobjective evolutionary algorithm is proposed for optimal power flow (OPF) including wind and solar generation uncertainty. This study recommends a novel constraint-handling method, which adaptively adds the penalty function and eliminates the parameter dependency on penalty function evaluation. The summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of multiobjective optimization algorithms. The OPF problem is modeled as a multiobjective optimization problem with four objectives such as minimizing (i) total fuel cost (TC) including the cost of renewable energy source (RES), (ii) total emission (TE), (iii) active power loss (APL), and (iv) voltage magnitude deviation (VMD). The impact of RESs such as wind and solar energy sources on integration is considered in optimal power flow cost analysis. The costs of RESs are considered in the OPF problem to minimize the overall cost so that the impact of intermittence and uncertainty of renewable sources is studied in terms of cost and operation wise. The uncertainty of wind and solar energy sources is described using probability distribution functions (PDFs) such as Weibull and lognormal distributions. The efficiency of the algorithm is tested on IEEE 30-, IEEE 57-, and IEEE 118-bus systems for all possible conditions of renewable sources using Monte Carlo simulations.

Topology and size optimization for X-bracing system of nonlinear inelastic space steel frames

In this article, a Python-programmed advanced design paradigm is firstly introduced to topology and size optimization of the X-bracing system of nonlinear inelastic space steel frames. For that purpose, an advanced analysis method considering both geometric and material nonlinearities is utilized as an effective finite element analysis (FEA) solver. In which, X-bracing members are modeled by truss elements, while the beam and column members are simulated by beam-column ones. The bracing members’ cross-sectional area and their position are respectively treated as discrete size and topology design variables. The problem aims to minimize the weight of X-bracing system so that the constraints on the strength, inter-story drift and maximum displacement are satisfied. An adaptive hybrid evolutionary firefly algorithm (AHEFA) is employed as an optimizer. Numerical examples are exhibited to illustrate the powerful ability of the present methodology.

A Hybrid Evolutionary Algorithm for the Diversified Top-k Weight Clique Search Problem (Student Abstract)

The diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique search problem, which extends the DTKC search problem by taking into account the weight of vertices. This problem involves finding at most k maximal weighted cliques that cover maximum weight of vertices with low overlapping in a given graph. In this study, a mixed integer linear program constraint formulation is proposed to model DTKWC search problem and an efficient hybrid evolutionary algorithm (HEA-D) based on some heuristic strategies is proposed to tackle it. Experiments on two sets of 110 graphs show that HEA-D outperforms the state-of-art methods.

Disease-Ligand Identification Based on Flexible Neural Tree.

In order to screen the disease-related compounds of a traditional Chinese medicine prescription in network pharmacology research accurately, a new virtual screening method based on flexible neural tree (FNT) model, hybrid evolutionary method and negative sample selection algorithm is proposed. A novel hybrid evolutionary algorithm based on the Grammar-guided genetic programming and salp swarm algorithm is proposed to infer the optimal FNT. According to hypertension, diabetes, and Corona Virus Disease 2019, disease-related compounds are collected from the up-to-date literatures. The unrelated compounds are chosen by negative sample selection algorithm. ECFP6, MACCS, Macrocycle, and RDKit are utilized to numerically characterize the chemical structure of each compound collected, respectively. The experiment results show that our proposed method performs better than classical classifiers [Support Vector Machine (SVM), random forest (RF), AdaBoost, decision tree (DT), Gradient Boosting Decision Tree (GBDT), KNN, logic regression (LR), and Naive Bayes (NB)], up-to-date classifier (gcForest), and deep learning method (forgeNet) in terms of AUC, ROC, TPR, FPR, Precision, Specificity, and F1. MACCS method is suitable for the maximum number of classifiers. All methods perform poorly with ECFP6 molecular descriptor.

Self-adaptive coordination for fuel-constrained robot teams with periodic and aperiodic communications

This paper introduces a novel mission-planning scheme to coordinate a heterogeneous team of mobile robots under fuel and communication constraints. The proposed scheme has a two-level structure, where the upper level is responsible for scheduling team communication, recharging and regrouping activities, while the lower level provides plans and routes for individual group members. Such decomposition allows us to relieve an overall group routing problem by singling out the harshest constraints as a separate time-management problem. Thus, the high-level planning problem is to construct a group schedule ensuring both the efficient refuelling order and the required communication periodicity. The problem is formulated in terms of workforce scheduling problems and a hybrid evolutionary algorithm is developed to solve it. The lower-level module is to arrange emergent team communication sessions in case of unpredicted events. This original problem combines features of vehicle routing problems and special aspects of multi-agent network spreading models. A few simple ruled-based heuristics are presented to treat the problem in a timely manner.

PESA: Prioritized experience replay for parallel hybrid evolutionary and swarm algorithms - Application to nuclear fuel

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.

Switching strategy-based hybrid evolutionary algorithms for job shop scheduling problems

Since production efficiency and costs are directly affected by the ways in which jobs are scheduled, scholars have advanced a number of meta-heuristic algorithms to solve the job shop scheduling problem (JSSP). Although this JSSP is widely accepted as a computationally intractable NP-hard problem in combinatorial optimization, its solution is essential in manufacturing. This study proposes performance-driven meta-heuristic switching approaches that utilize the capabilities of multi-operator differential evolution (MODE) and particle swarm optimization (PSO) in a single algorithmic framework. The performance-driven switching mechanism is introduced to switch the population from an under-performing algorithm to other possibilities. A mixed selection strategy is employed to ensure the diversity and quality of the initial population, whereas a diversity check mechanism maintains population diversity over the generations. Moreover, a Tabu search (TS) inspired local search technique is implemented to enhance the proposed algorithm’s exploitation capability, avoiding being trapped in the local optima. Finally, this study presents two mixed population structure-based hybrid evolutionary algorithms (HEAs), such as a predictive sequence HEA (sHEA) and a random sequence HEA (rHEA), and one bi-population inspired HEA, called bHEA. The comparative impacts of these varied population structure-based approaches are assessed by solving 5 categories of the standard JSSP instances (i.e., FT, LA, ORB, ABZ and TA). The performance of these hybridized approaches (i.e., sHEA, rHEA and bHEA) is compared and contrasted with its constituent algorithms (MODE, PSO and TS) to validate the hybridization’s effectiveness. The statistical analysis shows that sHEA ranked first with mean value 1.84 compared to rHEA (1.96) and bHEA (2.21). Moreover, the proposed sHEA is compared with 26 existing algorithms and ranked first with a mean value 5.09 compared to the near-best algorithms. Thus, the simulation results and statistical analysis prove the supremacy of the sHEA.

A hybrid evolutionary algorithm for the capacitated minimum spanning tree problem

The capacitated minimum spanning tree (CMST) problem is a fundamental problem in telecommunication network design. Given a central node and a set of remote terminal nodes with specified demands for traffic, the goal of CMST is to find a minimum cost spanning tree (network) such that the traffic on any arc of the network satisfies the capacity constraint. To tackle this NP-hard problem, we propose an effective hybrid evolutionary algorithm that integrates a backbone-based crossover operator, a destroy-and-repair mutation operator to generate meaningful offspring solutions, and an intensification-driven adaptive tabu search procedure that considers both feasible and infeasible solutions in search of high-quality local optima. Extensive computational results on a set of 126 well-known CMST benchmark instances from the literature indicate that the proposed algorithm competes favorably with the state-of-the-art heuristics. For a selection of 25 most challenging CMST instances with unknown optimal solutions, the proposed algorithm reports new upper bounds (improved best-known solutions) in 9 cases, while reaching the best-known result for 12 instances. Furthermore, we provide experimental analyses to identify the key algorithmic features of the proposed approach.

An Efficient Hybrid Evolutionary Optimization Method Coupling Cultural Algorithm with Genetic Algorithms and Its Application to Aerodynamic Shape Design

Evolutionary algorithms have been widely used to solve complex engineering optimization problems with large search spaces and nonlinearity. Both cultural algorithm (CA) and genetic algorithms (GAs) have a broad prospect in the optimization field. The traditional CA has poor precision in solving complex engineering optimization problems and easily falls into local optima. An efficient hybrid evolutionary optimization method coupling CA with GAs (HCGA) is proposed in this paper. HCGA reconstructs the cultural framework, which uses three kinds of knowledge to build the belief space, and the GAs are used as an evolutionary model for the population space. In addition, a knowledge-guided t-mutation operator is developed to dynamically adjust the mutation step and introduced into the influence function. HCGA achieves a balance between exploitation and exploration through the above strategies, and thus effectively avoids falling into local optima and improves the optimization efficiency. Numerical experiments and comparisons with several benchmark functions show that the proposed HCGA significantly outperforms the other compared algorithms in terms of comprehensive performance, especially for high-dimensional problems. HCGA is further applied to aerodynamic optimization design, with the wing cruise factor being improved by 23.21%, demonstrating that HCGA is an efficient optimization algorithm with potential applications in aerodynamic optimization design.

Dissolved organic nitrogen, dinoflagellates and cyanobacteria in two eutrophic lakes: Analysis by inferential modelling

The Lake Suwa (Japan) has a history of non-N-fixing Microcystis blooms. Lake Kinneret (Israel) experienced multiannual periods of sole domination by the dinoflagellate Peridinium gatunense and periods dominated seasonally by P. gatunense or cyanobacteria. Extensive studies have been carried out in both lakes regarding the role of dissolved inorganic nitrogen and phosphorus as drivers of primary productivity. There is growing evidence that dissolved organic nitrogen (DON) compounds also influence not only biomass and structure of phytoplankton communities but also microcystin production. This study focuses on relationships of DON with: (1) population dynamics of Microcystis spp. and concentrations of microcystins in Lake Suwa, and (2) population dynamics of P. gatunense as well as N- and non-N-fixing cyanobacteria in Lake Kinneret. Modelling results for historical data of Lake Suwa by means of the hybrid evolutionary algorithm HEA revealed that the prediction of abundances of four Microcystis species and concentrations of cyanotoxins achieved higher coefficients of correlation when DON/DIN-ratios were included as drivers. Population dynamics of P. gatunense in Lake Kinneret appeared to have a strong inverse relationships with DON/DIN-ratios reflected by inferential models of HEA with higher coefficients of correlation when driven by DON/DIN-ratios. When DON/DIN-ratios were included as drivers, models of Microcystis spp. in Lake Kinneret performed higher coefficients of determination compared to models of N-fixing cyanobacteria. The study highlights the need to consider DON for improved understanding and management of population dynamics of cyanobacteria and dinoflagellates in freshwater lakes.

Applied heat exchanger network retrofit for multi-period processes in industry: A hybrid evolutionary algorithm

In Swiss process industry, process integration is often applied to retrofit existing plants with multi-period operation. Such periods may experience a high degree of variation in temperature or mass flow. Some process streams may not exist in every period or are soft streams. The resulting retrofitted network needs to be able to ensure feasible heat transfer in each period by the integration of mixer configurations to control the temperature. These attributes increase the complexity of the solution space. Hence, this work proposes an evolutionary two-level algorithm for heat exchanger network retrofit. Genetic algorithm is used for topology optimization and a differential evolution algorithm handles the heat loads. The algorithm is extended with practical constraints such as a maximum number of heat exchangers. Explicit mixer temperature calculations are implemented using the Lambert W-function. The algorithm was successfully applied to an industrial case study, reducing its total annual cost by approximately 66%.

Ternary selection based Hybrid Evolutionary Algorithm for maximizing water retention on magic squares

Ternary selection based Hybrid Evolutionary Algorithm for maximizing water retention on magic squares

A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems

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

Hybrid evolutionary algorithm with sequence difference-based differential evolution for multiobjective fuzzy flow-shop scheduling problem

Hybrid evolutionary algorithm with sequence difference-based differential evolution for multiobjective fuzzy flow-shop scheduling problem

Hybrid evolutionary algorithm with sequence difference-based differential evolution for multi-objective fuzzy flow-shop scheduling problem

Hybrid evolutionary algorithm with sequence difference-based differential evolution for multi-objective fuzzy flow-shop scheduling problem

Order batching and order picking with 3D positioning of the articles: solution through a hybrid evolutionary algorithm.

A critical factor in the logistic management of firms is the degree of efficiency of the operations in distribution centers. Of particular interest is the pick-up process, since it is the costliest operation, amounting to 50 and up to 75% of the total cost of the activities in storage facilities. In this paper we jointly address the order batching problem (OBP) and the order picking problem (OPP). The former problem amounts to find optimal batches of goods to be picked up, by restructuring incoming orders by either splitting up large orders or combining small orders into larger ones that can then be picked in a single picking tour. The OPP, in turn, involves identifying optimal sequences of visits to the storage positions in which the goods to be included in each batch are stored. We seek to design a plan that minimizes the total operational cost of the pick-up process, proportional to the displacement times around the storage area as well as to all the time spent in pick-ups and finishing up orders to be punctually delivered. Earliness or tardiness will induce inefficiency costs, be it because of the excessive use of space or breaches of contracts with customers. Tsai, Liou and Huang in 2008 have generated 2D and 3D instances. In previous works we have addressed the 2D ones, achieving very good results. Here we focus on 3D instances (the articles are placed at different levels in the storage center), which involve a higher complexity. This contributes to improve the performance of the hybrid evolutionary algorithm (HEA) applied in our previous works.

AI-based modelling and optimisation of rotating magnetic force assisted powder mixed EDM

Though widely used to process newly developed materials, electrical discharge machining (EDM) possesses poor machining efficiency. Modifications/hybridisation of EDM can improve its performance with respect to different quality parameters. Mixing powder in dielectric and applying magnetic field near machining zone are few of EDM modifications. The present paper investigates the performance of EDM with above mentioned modifications applied simultaneously. The innovative setup was designed and fabricated to perform rotary magnetic field assisted powder mixed EDM (MFPMEDM). The experiments were conducted on aluminium titanium di-boride metal matrix composite to evaluate MRR by varying different process parameters including powder concentration under the influence of magnetic field. The performance of both EDM and MFPMEDM has been compared and latter has been found much better in terms of process efficiency. Further, hybrid evolutionary optimisation algorithm has been used for modelling and optimisation of MRR.