Multi-population Evolutionary Algorithm Research Articles

A novel multi-population evolutionary algorithm based on hybrid collaboration for constrained multi-objective optimization

Multi-population-based methods are widely employed for solving constrained multiobjective optimization problems (CMOPs). The population collaboration strategy is a critical part of multi-population algorithms, and different collaboration strategies perform well on different complex CMOPs. However, these single-population collaboration strategies are still challenging to adapt to various CMOPs with different characteristics. To address this issue, we propose a novel tri-population hybrid collaboration evolutionary algorithm called TP-HCEA, which includes a constraint-relaxed population (denoted as mainpop), a constraint-ignored auxiliary population (denoted as auxpop1), and an auxiliary population (denoted as auxpop2) for the original CMOP, to search optimal solutions in the feasible region. Specifically, due to the different complementarities of the two auxiliary populations, mainpop collaborates with auxpop1 and auxpop2 in a dynamic choice between strong and weak cooperation. The effectiveness of TP-HCEA is validated through comparative analysis with seven state-of-the-art algorithms in four CMOP benchmark suites and nine real-world problems.

A self-learning multi-population evolutionary algorithm for flexible job shop scheduling under time-of-use pricing

Due to the energy crisis and environmental downgrade, manufacturing companies face rising costs. The pressure on manufacturers to reduce costs and increase efficiency is increasing. The flexible job shop scheduling problem considering time-of-use pricing (FJSPTOUP) is studied to minimize the maximum completion time and total electricity cost. A new self-learning multi-population evolutionary algorithm (SLMPEA) is proposed to address the static scheduling problem. A self-learning module is designed to acquire knowledge from elite individuals in subpopulations. The knowledge discovered can guide the evolution of subpopulations. To improve the convergence of the algorithm and maintain the diversity of the population, we present an elite retention and enhancement strategy. Two enhancement operators are developed to elevate the quality of elite individuals, and a selection strategy is designed for the enhancement operators. A time-of-use-based adjustment strategy is proposed to reduce the total electricity costs without increasing the maximum completion time. Experimental results show that the proposed SLMPEA outperforms the comparative algorithms in solving FJSPTOUP.

A robustness division based multi-population evolutionary algorithm for solving vehicle routing problems with uncertain demand

The vehicle routing problem with uncertain demand (VRPUD) is an extension of capacitated vehicle routing problem (CVRP), where the demand of each customer is unknown when dispatching the vehicles to service customers. Since it is more practical than CVRP, the VRPUD has aroused wide attention. Although the evolutionary algorithms (EAs) have demonstrate its promising performance on solving VRPUD, the most of EAs only consider the robustness of solution after generating offspring, which limit the quality of solutions found by EAs. To this end, in this paper, a robustness division based multi-population evolutionary algorithm (RDMPEA) is developed for VRPUDs, where the robustness is considered before, during and after offspring. Specifically, before generating offspring, the RDMPEA first divides the individuals into different subpopulations according to their robustness level, and only the individuals within the same subpopulation can match each other and generate offspring. During generating offspring, the RDMPEA employs a route based crossover operator to generate offspring, where the routes with higher robustness have a greater probability of being inherited by the offspring. After generating offspring, a dedicated environment selection strategy is applied to survive the individuals with better robustness and travel cost. In the experiments, the proposed RDMPEA is compared to three state-of-the-art heuristic methods tailored for VRPUDs on a variety of instances obtained by using three widely used vehicle routing problem benchmarks. The experimental results indicate that the proposed RDMPEA is superior to three compared algorithms, and can find solutions with better travel cost and robustness.

A Multipopulation Evolutionary Algorithm Using New Cooperative Mechanism for Solving Multiobjective Problems With Multiconstraint

In science and engineering, multi-objective optimization problems usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This paper aims to solve the challenges brought by multiple complex constraints. First, this paper analyzes the relationship between single constrained Pareto Front (SCPF) and their common Pareto Front sub-constrained Pareto Front (SubCPF). Next, we discussed the SCPF, SubCPF, and Unconstrainti Pareto Front (UPF)’s help to solve constraining Pareto Front (CPF). Then further discusses what kind of cooperation should be used between multiple populations constrained multi-objective optimization algorithm (CMOEA) to better deal with multi-constrained multi-objective optimization problems (mCMOPs). At the same time, based on the discussion in this paper, we propose a new multi-population CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed Activation Dormancy Detection (ADD) to accelerate the optimization process and uses the proposed Combine Occasion Detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.

MPEA-FS: A decomposition-based multi-population evolutionary algorithm for high-dimensional feature selection

The challenge of high-dimensional feature selection (FS) lies in the search technique, which needs to consider both minimizing the size of feature subset and maximizing the classification accuracy. Recently, multi-objective evolutionary algorithms (MOEAs) have shown excellent performance in solving FS tasks. However, most existing MOEAs struggle to effectively balance these two conflicting objectives when solving high-dimensional FS tasks. To cover this issue, in this paper, a decomposition-based multi-population evolutionary algorithm is proposed, called MPEA-FS. In the initialization stage, a multi-population generation strategy (MGS) based on feature weights (MGS) was adopted, with each population corresponding to a search subspace, which can improve the algorithm’s search ability. During the evolutionary stage, an external population is designed to integrate the excellent feature subsets of multiple populations to achieve knowledge sharing between them. In addition, to reduce the feature dimension, a feature reduction strategy (FRS) is proposed, which can remove unimportant features while maintaining classification accuracy. Extensive experiments are performed on 13 high-dimensional datasets, and the proposed algorithm is compared with 5 state-of-the-art FS methods proposed in the last 3 years on multiple metrics. The experimental results indicate that MPEA-FS can achieve higher classification accuracy on most datasets, and the number of selected features is also competitive.

A multi-population evolutionary algorithm for multi-objective constrained portfolio optimization problem

A multi-population evolutionary algorithm for multi-objective constrained portfolio optimization problem

A novel three-stage multi-population evolutionary algorithm for constrained multi-objective optimization problems

Lots of real-world optimization problems are inherently constrained multi-objective optimization problems (CMOPs), but the existing constrained multi-objective optimization evolutionary algorithms (CMOEAs) often fail to balance convergence and diversity effectively. Therefore, a novel constrained multi-objective optimization evolutionary algorithm based on three-stage multi-population coevolution (CMOEA-TMC) for complex CMOPs is proposed. CMOEA-TMC contains two populations, called mainPop and helpPop, which evolve with and without consideration of constraints, respectively. The proposed algorithm divides the search process into three stages. In the first stage, fast convergence is achieved by transforming the original multi-objective problems into multiple single-objective problems. Coarse-grained parallel evolution of subpopulations in mainPop and guidance information provided by helpPop can facilitate mainPop to quickly approach the Pareto front. In the second stage, the objective function of mainPop changes to the original problem. Coevolution of mainPop and helpPop by sharing offsprings can produce solutions with better diversity. In the third stage, the mining of the global optimal solutions is performed, discarding helpPop to save computational resources. For CMOEA-TMC, the combination of parallel evolution, coevolution, and staging strategy makes it easier for mainPop to converge and maintain good diversity. Experimental results on 33 benchmark CMOPs and a real-world boiler combustion optimization case show that CMOEA-TMC is more competitive than the other five advanced CMOEAs.

PRETTY: A parallel transgenerational learning-assisted evolutionary algorithm for computationally expensive multi-objective optimization

Many real-world optimization problems, called computationally expensive optimization problems, often require a time-consuming fitness evaluation through computer simulation or neural network training. A persistent challenge is to obtain reasonable solutions to these problems quickly. In this study, we design a parallel transgenerational learning-assisted evolutionary algorithm, called PRETTY, to accelerate the search process using three novel mechanisms. First, we fuse the transgenerational learning mechanism with metaheuristic algorithms to reduce unnecessary fitness evaluations. Then, a Hypervolume-Cauchy (HV-Cauchy) algorithm is proposed to balance population convergence and diversity adaptively. Finally, a parallel-computing framework is proposed to fuse the transgenerational learning mechanism and the HV-Cauchy algorithm into a multipopulation evolutionary algorithm. To verify the performance of the proposed algorithm, empirical experiments are conducted on nine benchmarks and four real-world problems, including feature selection, critical node detection, flocking, and neural network training. In terms of execution time, the experimental results demonstrate that PRETTY has significant advantages over several expensive multi-objective algorithms. Furthermore, PRETTY can maintain population diversity and fast convergence while accelerating the computation. In particular, in tackling real-world problems that require expensive fitness calculations, PRETTY can reduce the execution time by more than five times while ensuring high-quality solutions.

COVID-19 forecasting and intervention planning using gated recurrent unit and evolutionary algorithm.

The rapid spread of COVID-19, caused by the SARS-CoV-2 virus, has had and continues to pose a significant threat to global health. We propose a predictive model based on the gated recurrent unit (GRU) that investigates the influence of non-pharmaceutical interventions (NPIs) on the progression of COVID-19. The proposed model is validated by case studies for multiple states in the United States. It should be noted that the proposed model can be generalized to other regions of interest. The results show that the predictive model can achieve accurate forecasts across the US. The forecast is then utilized to identify the optimal mitigation policies. The goal is to identify the best stringency level for each policy that can minimize the total number of new COVID-19 cases while minimizing the mitigation costs. A meta-heuristics method, named multi-population evolutionary algorithm with differential evolution (MPEA-DE), has been developed to identify optimal mitigation strategies that minimize COVID-19 infection cases while reducing economic and other negative implications. We compared the optimal mitigation strategies identified by the MPEA-DE model with three baseline search strategies. The results show that MPEA-DE performs better than other baseline models based on prescription dominance.

Dual-Population Artificial Bee Colony Algorithm for Joint Observation Satellite Mission Planning Problem

Earth observation satellites provide services for users by taking images. The rapid increase in the number of satellites and missions makes mission planning more difficult. In order to solve this problem, this paper adopts a multi-population evolutionary algorithm. First, a mixed-integer programming model of the joint observation satellites mission planning problem (JOSMPP) is constructed. After that, a dual-population artificial bee colony algorithm (DPABC) and a heuristic task scheduling algorithm are proposed. Two learning-based nectar generation methods are used to guide the direction of population optimization. The search population is used to lead the nectar search at the stage of employed bees and supplement population is used to improve search performance. According to the performance of these two populations, candidate populations are generated to realize the search of onlooker bees. After each generation of population search, the composition of the two populations is adjusted according to the performance. Finally, the experimental results show that the DPABC algorithm has more advantages than multiple state-of-the-art algorithms to solve the joint mission planning problem of earth observation satellites.

Adaptive Multipopulation Evolutionary Algorithm for Contamination Source Identification in Water Distribution Systems

AbstractReal-time monitoring of drinking water in a water distribution system (WDS) can effectively warn of and reduce safety risks. One of the challenges is to identify the contamination source th...

Building Robust Closed-Loop Supply Networks against Malicious Attacks

With recent industrial upgrades, it is essential to transform the current forward supply networks (FSNs) into closed-loop supply networks (CLSNs), which are formed by the integration of forward and reverse logistics. The method chosen in this paper for building reverse logistics is to add additional functions to the existing forward logistics. This process can be regarded as adding reverse edges to the original directed edges in an FSN. Due to the limitation of funds and the demand for reverse flow, we suppose that a limited number of reverse edges can be built in a CLSN. To determine the transformation schemes with excellent robustness against malicious attacks, this paper proposes a multi-population evolutionary algorithm with novel operators to optimize the robustness of the CLSN, and this algorithm is abbreviated as MPEA-RSN. Then, both the generated and realistic SNs are taken as examples to validate the effectiveness of MPEA-RSN. The simulation results show that the index R, introduced to evaluate the robustness of CLSNs, can be improved by more than 95%, and this indicates that (1) the different schemes for adding reverse routes to an FSN can lead to different robustness values, and (2) the robustness of the transformed CLSN to malicious attacks can be significantly improved after optimization by MPEA-RSN. When an FSN is to be transformed into a CLSN, this paper can provide a frame of reference for building a CLSN that is robust to malicious attacks from a network structural perspective.

A Multipopulation Evolutionary Algorithm for Solving Large-Scale Multimodal Multiobjective Optimization Problems

Multimodal multiobjective optimization problems (MMOPs) widely exist in real-world applications, which have multiple equivalent Pareto-optimal solutions that are similar in the objective space but totally different in the decision space. While some evolutionary algorithms (EAs) have been developed to find the equivalent Pareto-optimal solutions in recent years, they are ineffective to handle large-scale MMOPs having a large number of variables. This article thus proposes an EA for solving large-scale MMOPs with sparse Pareto-optimal solutions, i.e., most variables in the optimal solutions are 0. The proposed algorithm explores different regions of the decision space via multiple subpopulations and guides the search behavior of the subpopulations via adaptively updated guiding vectors. The guiding vector for each subpopulation not only provides efficient convergence in the huge search space but also differentiates its search direction from others to handle the multimodality. While most existing EAs solve MMOPs with 2-7 decision variables, the proposed algorithm is shown to be effective for benchmark MMOPs with up to 500 decision variables. Moreover, the proposed algorithm also produces a better result than state-of-the-art methods for the neural architecture search.

Multi-objective flexible job shop scheduling problem with triple bottom line theory-based sustainable objectives

The triple bottom line theory (TBL) is widely used in the field of sustainable development research. Based on the triple bottom line theory, this paper investigates a multi-objective flexible job shop scheduling problem with the consideration of sustainable objectives. A multi-population evolutionary algorithm with single-objective guide (MPEA/SG) is modified to solve this problem effectively by extending the MPEA/SG to discrete optimization problems. Our experiments show that considering TBL-based objectives in job shop scheduling is more conducive to the sustainability of the production process, and can effectively reduce the carbon footprint, E/T penalty cost and income difference.

Multi-population evolutionary algorithm SPEA2 for crystal structure determination from X-ray powder diffraction data

The problem of determining inorganic crystal structures with a large number of atoms from powder diffraction data is a relevant research task. In order to solve this problem, we made an algorithm of multi-objective optimization based on the SPEA2 approach implemented in a multi-threaded (parallel) version. This algorithm is a development of previously presented variants of single-criterion multi-population GA (MPGA). The article describes the algorithm and the results of applying to test crystal structures.

A multi-population evolutionary algorithm with single-objective guide for many-objective optimization

This paper develops a multi-population evolutionary algorithm with single-objective guide to tackle many-objective optimization problems. It exploits the merits of both multiple populations and single-objective optimization to balance diversity and convergence of the evolution process. Specifically, the single-objective guide process helps to construct the better ideal point and reference points. A novel objective space partitioning mechanism is developed to transform a many-objective optimization problem into multiple subproblems, each of which is tackled by a subpopulation. A novel information sharing mechanism between subpopulations is proposed to balance diversity and convergence. Finally the subpopulations are merged together and deal with many-objective optimization problems to further enhance the convergence. We have compared the performance of the proposed algorithm with nine state-of-the-art algorithms on 85 test instances of 21 benchmark problems with up to 15 objectives. Experimental results show that the proposed algorithm has the superior performance in solving multi- and many-objective optimization problems.

State Rules Mining and Probabilistic Fault Analysis for 5 MW Offshore Wind Turbines

Research on fault identification for wind turbines (WTs) is a widespread concern. However, the identification accuracy in existing research is vulnerable to uncertainty in the operation data, and the identification results lack interpretability. In this paper, a data-driven method for fault identification of offshore WTs is presented. The main idea is to improve fault identification accuracy and facilitate the probabilistic sorting of possible faults with critical variables so as to provide abundant and reliable reference information for maintenance personnel. In the stage of state rule mining, representative initial rules are generated via the combination of a clustering algorithm and heuristic learning. Then, a multi-population quantum evolutionary algorithm is utilized to optimize the rule base. In the stage of fault identification, abnormal states are identified via a fuzzy rule-based classification system, and probabilistic fault sorting with critical variables is realized according to the fuzzy reasoning of state rules. Ten common sensor and actuator faults in 5 MW offshore WTs are taken to verify the feasibility and superiority of the proposed scheme. Experimental results demonstrate that the proposed method has higher identification accuracy than other identification methods and thus prove the feasibility of the proposed probabilistic fault analysis scheme.

Full-Profile X-Ray Phase Analysis of the Composition of Aluminum Electrolyte on the Basis of a Multipopulation Evolutionary Algorithm and Elemental Analysis Data

Full-Profile X-Ray Phase Analysis of the Composition of Aluminum Electrolyte on the Basis of a Multipopulation Evolutionary Algorithm and Elemental Analysis Data

Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization

Cooperative co-evolution (CC) is an explicit means of problem decomposition in multipopulation evolutionary algorithms for solving large-scale optimization problems. For CC, subpopulations representing subcomponents of a large-scale optimization problem co-evolve, and are likely to have different contributions to the improvement of the best overall solution to the problem. Hence, it makes sense that more computational resources should be allocated to the subpopulations with greater contributions. In this paper, we study how to allocate computational resources in this context and subsequently propose a new CC framework named CCFR to efficiently allocate computational resources among the subpopulations according to their dynamic contributions to the improvement of the objective value of the best overall solution. Our experimental results suggest that CCFR can make efficient use of computational resources and is a highly competitive CCFR for solving large-scale optimization problems.

Clustering large probabilistic graphs using multi-population evolutionary algorithm

Determining valid clustering is an important research problem. This problem becomes complex if the underlying data has inherent uncertainties. The work presented in this paper deals with clustering large probabilistic graphs using multi-population evolutionary algorithm. The evolutionary algorithm (EA) initializes its multiple populations, each representing a deterministic version of the same probabilistic graph given to it as an input. Multiple deterministic versions of the same input graph are generated by applying different thresholds to the edges. Each chromosome of the multiple populations represents one complete clustering solution. For the purpose of clustering, EA is employed which is guided by pKwikCluster algorithm. The proposed approach is tested on two natively probabilistic graphs and nine synthetically converted probabilistic graphs using cluster validity indices of Davies–Bouldin index, Dunn index, and Silhouette coefficient. The proposed approach is also compared with two baseline clustering algorithms for uncertain data, Fuzzy-DBSCAN and uncertain K-mean and two state-of-the-art approaches for clustering probabilistic graphs. The results obtained suggest that the proposed solution gives better performance than the baseline methods and the state-of-the-art algorithms.