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The differential evolution (DE) algorithm is widely regarded as one of the most influential evolutionary algorithms for addressing complex optimization problems. However, the fixed mutation strategy limits the adaptive ability of DE, and the lack of utilization of historical information limits the optimization ability of DE. In this paper, an indicator-based self-learning differential evolution algorithm (ISDE) is proposed. A jump out mechanism based on deep reinforcement learning is adopted to control the mutation intensity of the population. The neural network in the jump out mechanism is designed as a decision maker. The mutation intensity of the population is controlled by the neural network, and the neural network are trained by a double deep Q network algorithm based on the continuous data generated during the evolution process. A population range indicator (PRI) is utilized to describe individual differences in the population. A diversity maintenance mechanism is designed to maintain individual differences according to the value of PRI. The experimental results reveal that the comprehensive performance of ISDE is superior to comparison algorithms on CEC 2017 real-parameter numerical optimization. [ABSTRACT FROM AUTHOR]

As a kind of environmental pollutant, heavy metal Cadmium (Cd) exists widely in the environment. It is well known that Cd can accumulate and cause damage in liver, kidney and other organs. However, there are few studies on the immune cytotoxicity of Cd to fish. In particular, there are few studies on the toxicity of Cd to the head kidney lymphocytes of common carp. In order to further explore these mechanisms, we established an Cd exposure model in vitro. At the same time, we used the natural antioxidant astilbin (AST) to treat the cells to study its antagonistic effect on the toxicity of Cd. After exposure to Cd, the level of oxidative stress in head kidney lymphocytes increased, and the mRNA and protein expression of apoptosis-related markers Fas, FADD, Caspase8 and Caspase3 increased significantly (P < 0.05), which led to lymphocytes apoptosis. Hoechst staining and AO/EB staining also showed that the level of apoptosis increased after exposure to Cd. This is consistent with our previous research results. AST treatment reduced oxidative stress and apoptosis induced by Cd. In addition, oxidative stress inhibitor NAC could also reduce head kidney lymphocytes apoptosis induced by Cd, indicating that oxidative stress was involved in this process. Our results suggested that AST can alleviate the apoptosis of carp head kidney lymphocytes induced by Cd through oxidative stress. This study enriches the theoretical mechanism of Cd toxicity to fish head kidney lymphocytes, and puts forward a method to solve the toxicity of Cd, which provides a theoretical and research basis for the in vivo study of animal models in the future. [Display omitted] • Cadmium induced apoptosis and oxidative stress of carp lymphocytes. • Astilbin could reduce apoptosis of lymphocytes induced by cadmium. • Oxidative stress was involved in apoptosis of lymphocytes induced by cadmium. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid cooperative differential evolution with the perturbation of the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) with the local search of Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) mechanism, named jSO_CMA-ES_LBFGS, is proposed to solve the complex continuous problems. In the proposed algorithm, jSO, as a variant of Differential Evolution (DE), is used as a global search operator to explore the entire solution space. When the population falls into stagnation, a relatively reliable initial solution for the local search operator is generated by the CMA-ES, which is activated to perturb the optimal candidates in the solution space. The LBFGS utilized as the local search strategy is embedded in CMA-ES to obtain the potential local optimal solutions. A cooperative co-evolutionary dynamic system is formed by jSO and CMA-ES with a local search operator. The proposed jSO_CMA-ES_LBFGS is tested on the CEC2017 benchmark test suite and compared with eleven state-of-the-art algorithms. Further, two practical engineering problems are investigated utilizing the proposed method. The experimental results reveal the effectiveness and efficiency of the jSO_CMA-ES_LBFGS. [ABSTRACT FROM AUTHOR]

The invasive weed algorithm (IWO) is a meta-heuristic algorithm, which is an effective and promising optimiser to address the optimisation problems. In this study, a hybrid algorithm based on the self-adaptive invasive weed algorithm (IWO) and differential evolution algorithm (DE), named SIWODE, is proposed to address the continuous optimisation problems. In the proposed SIWODE, first, the two parameters are adaptively proposed to improve the convergence speed of the algorithm. Second, the crossover and mutation operations are introduced in SIWODE to improve the population diversity and increase the exploration capability during the iterative process. Furthermore, a local perturbation strategy is presented to improve exploitation ability during the late process. The exploration and exploitation ability of the algorithm is effectively balanced by cooperative mechanisms. The experiment results of SIWODE show that the SIWODE has the superior searching quality and stability than other mentioned approaches. [ABSTRACT FROM AUTHOR]

The influence maximization problem in social networks aims to select a subset of most influential nodes, denoted as seed set, to maximize the influence diffusion of the seed nodes. The majority of existing works on this problem would ignite all the seed nodes simultaneously at the beginning of the diffusion process and let the influence diffuses passively in the network. However, it cannot depict the practical dynamics exactly of viral marketing campaigns in reality and fails to provide driving policies to control over the diffusion. In this paper, we focus on the dynamic influence maximization problem with limited budget to study the scheduling strategies including which influential node is to be seeded during the diffusion process and when to seed it at the right time. A time-dependent seed activating feedback scheme is modeled firstly by considering the time factor and its impact on the influence obligation in diffusion process. Then a scheduling heuristic based on determinate and latent margin is proposed to evaluate the marginal return of candidate nodes and activate the right seed node to promote the viral marketing. Extensive experiments on four social networks show that the proposed algorithm achieves significantly better results than a typical static influence maximization algorithm based on swarm intelligence and can improve the influence propagation under the time-dependent diffusion model comparing with the centrality-based scheduling heuristics. [ABSTRACT FROM AUTHOR]

Background: Postpartum depression (PPD) has been identified as a recognized public health problem that may adversely affect mothers, infants, and family units. Recent studies have identified risk factors for PPD in Westerners; however, societal and cultural differences between China and the West could, potentially, lead to differences in risk factors for PPD. No comprehensive study has been conducted to collect all the evidence to provide estimates of psychological and social risk factors in China. Therefore, this study aimed to quantitatively assess all studies meeting the review's eligibility criteria and identify the psychological and social risk factors for PPD in Chinese women.Methods: The following databases were used in the literature search from their inception until December 2020: PubMed, Embase, Foreign Medical Literature Retrieval Service (FMRS), China Science and Technology Journal Database (VIP), China National Knowledge Infrastructure (CNKI), and China Biology Medicine disc (CBM). The quality was assessed through Newcastle-Ottawa quality assessment scale. The I2statistic was used to quantify heterogeneity. We extracted data for meta-analysis and generated pooled-effect estimates from a fixed-effects model. Pooled estimates from a random-effects model were also generated if significant heterogeneity was present. Funnel plot asymmetry tests were used to check for publication bias. Statistical analysis was conducted using Review Manager version 5.3 software.Results: From a total of 1175 identified studies, 51 were included in the analysis. Prenatal depression (OR 7.70; 95% CI 6.02-9.83) and prenatal anxiety (OR 7.07; 95% CI 4.12-12.13) were major risk factors for PPD. A poor economic foundation (OR 3.67; 95% CI 3.07-4.37) and a poor relationship between husband and wife (OR 3.56; 95% CI 2.95-4.28) were moderate risk factors. Minor risk factors included a poor relationship between mother-in-law and daughter-in-law (OR 2.89; 95% CI 2.12-3.95), a lack of social support (OR 2.57; 95% CI 2.32-2.85), unplanned pregnancy (OR 2.55; 95% CI 2.08-3.14), and poor living conditions (OR 2.44; 95% CI 1.92-3.10), mother-in-law as the caregiver (1.95; 95% CI 1.54-2.48) .Conclusions: This study demonstrated a number of psychological and social risk factors for PPD in Chinese women. The major and moderate risk factors are prenatal depression, prenatal anxiety, a poor economic foundation, and a poor relationship between husband and wife. These findings have potential implications for informing preventive efforts and modifying screening to target at-risk populations. [ABSTRACT FROM AUTHOR]

The distributed flow shop scheduling problem has become one of the key problems related to the high efficiency impacted factor in the manufacturing industry due to its typical scenarios in real-world industrial applications. In this paper, a knowledge-driven cooperative scatter search (KCSS) is proposed to address the distributed blocking flow shop scheduling problem (DBFSP) to minimize the makespan. The scatter search (SS) is adopted as the basic optimization framework in KCSS. The neighborhood perturbation operator and the Q-learning algorithm are combined to select the appropriate perturbation operator in the search process. Firstly, considering the complexity of distributed scenarios, five search operators are used to construct a disturbance strategy pool. Secondly, the Q-learning algorithm dynamically chooses disturbance strategies to enhance exploration ability and search efficiency. Afterward, a local search method based on neighborhood reconstruction is proposed to perturb the currently found optimal solution to strengthen the ability of KCSS to develop in local areas. In addition, the path relinking mechanism is introduced into the subset combination method to guarantee the diversity of solutions in the optimization process. Finally, the performance of the KCSS algorithm is verified on the benchmark set, and the experimental results demonstrate the robustness and effectiveness of the KCSS algorithm. In addition, 518 of the best-known solutions out of 720 benchmark instances are updated. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) and Estimation of distribution algorithm (EDA) exhibit complementary superiority in solving complex continuous optimization and engineering problems. The design of appropriate strategies coordinated with the two algorithms to balance exploration and exploitation is conducive to obtaining high-precision solutions. A knowledge-driven co-evolutionary algorithm assisted by a cross-regional interactive learning mechanism (KCACIL) is proposed to achieve a comprehensive collaboration between the algorithms, diverse strategies, and cross-regional individuals. Various elite-guided mutation strategies and a self-feedback strategy based on successful experience in light of implicit knowledge are devoted to fulfilling self-learning and cross-regional interactive learning to accomplish individual collaboration and knowledge transfer in the three regions. Reinforcement learning based on ε − g r e e d y and simulated annealing is employed as feedback on the cross-regional individual information to promote the collaboration between opposition-based learning, interaction learning mechanism, and the revised strategy of inferior solutions with small Q values and high distance density. The dynamic self-adaptive adjustment strategies of multiple parameters are adopted to balance diversity and convergence. KCACIL is verified on the CEC 2014, 2017, 2020 benchmark test suites, and engineering applications. Experimental results indicate KCACIL is superior to the state-of-the-art comparison algorithms. • A knowledge-driven comprehensive co-evolutionary algorithm is proposed. • Cross-regional interactive learning with adaptive parameters is introduced. • A collaboration of strategies guided by reinforcement learning is implemented. • The learning mechanisms based on intra-regional implicit knowledge are designed. • Two improvement strategies of solutions specific to the phenomenon are employed. [ABSTRACT FROM AUTHOR]

A co-evolutionary migrating birds optimization algorithm based on online learning policy gradient (CMBO-PG) is proposed to address complex continuous real-parameter optimization problems. In CMBO-PG, a Gaussian estimation of distribution algorithm (GEDA), which enhances the exploitation tendency, is utilized to generate the solutions of the leading flock. The neighborhood solutions of the following flock are produced by a multi-strategy learning mechanism to promote exploration capability. The co-evolution of the leading flock and following flock is realized by the information-sharing mechanism and the operation of destruction and construction to keep the balance of exploration and exploitation. The nonlinear selection of mutation strategies is laborious due to the differences in the ability to address optimization problems. In the mechanism of multi-strategy learning, a long short-term memory (LSTM) is adopted as a selector of mutation strategies to predict the selection probability of three mutation strategies. The evolutionary procedure of the following flock is modeled as a Markov decision process (MDP). The policy gradient (PG) is employed as a model optimizer to control the parameters of LSTM based on the historical feedback information. The performance of CMBO-PG is testified on the CEC 2017 benchmark test suite. The experimental results show that CMBO-PG is superior to the 12 comparison algorithms, including state-of-art algorithms. [ABSTRACT FROM AUTHOR]

Water Wave Optimisation algorithm (WWO) is a new swarm-based metaheuristic inspired by shallow wave models for global optimisation. In this paper, an enhanced WWO, which combines with multiple assistant strategies (EWWO), is proposed. First, the random opposition-based learning (ROBL) mechanism is introduced to generate the initial population with high quality. Second, a new modified operation is designed and embedded into propagation operation to balance the global exploration and the local exploitation. Third, the covariance matrix self-adaptation evolution strategy (CMA-ES) is employed by the refraction operation to further strengthen the local exploitation. Furthermore, the diversity of the population is maintained in the evolution process by using a crossover operator. The experiment results based on CEC 2017 benchmarks indicate that the EWWO outperforms the state-of-the-art variant algorithms of the WWO and the standard WWO. [ABSTRACT FROM AUTHOR]

The iterated local search (ILS) is exceptionally successful in combinatorial solution spaces. However, few research works have reported on the application of ILS in continuous problems. In this article, a new hybrid population-based iterated local search (HILS) algorithm is proposed for solving numerical optimization problems. The proposed hybrid method introduces both success-history based parameter adaptation for differential evolution (SHADE) and limited-memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) as the perturbation and local search strategy, respectively, and integrates the benefits of exploration capability of SHADE and local search performance of LBFGS. The simulated annealing type of acceptance criterion is adopted to balance the exploration and exploitation of ILS. The proposed HILS is tested against the CEC2017 benchmark functions, which were used to evaluate the performance of the proposed algorithm in solving numerical optimization problems. The experimental results show the effectiveness and efficiency of the HILS. [ABSTRACT FROM AUTHOR]

In this article, a water wave optimization algorithm with a single wave mechanism, called single water wave optimization (SWWO), is proposed to solve the no-wait flow-shop scheduling problem (NWFSP) with the objective of minimizing the makespan. In the proposed SWWO, an improved Nawaz–Enscore–Ham (NEH) heuristic is applied to construct a high-quality initial candidate. In the propagation operation, a self-adaptive block-shift operation is employed. In the breaking operation, a variable neighbourhood search operation is utilized to explore the local optimal solution. According to the schema theory as presented in genetic algorithms, a crossover operation is adopted as the refraction operation. Finally, the computational results based on several benchmarks and statistical performance comparisons are presented. The experimental results demonstrate the effectiveness and efficiency of the proposed SWWO for solving the NWFSP. [ABSTRACT FROM AUTHOR]

Highlights • A HBV algorithm is proposed for solving NWFSP. • The migration operator is redifined by path relink and self-improvement strategy. • The IG is embedded into mutation operator to balance the exploration and exploitation. • A modified VNS is designed to enhance the exploration ability. • The convergence performance of HBV is analyzed with the Markov model. Abstract The no-wait flow shop scheduling problem (NWFSP) plays an essential role in the manufacturing industry. Inspired by the overall process of biogeography theory, the standard biogeography-based optimization (BBO) was constructed with migration and mutation operators. In this paper, a hybrid biogeography-based optimization with variable neighborhood search (HBV) is implemented for solving the NWFSP with the makespan criterion. The modified NEH and the nearest neighbor mechanism are employed to generate a potential initial population. A hybrid migration operator, which combines the path relink technique and the block-based self-improvement strategy, is designed to accelerate the convergence speed of HBV. The iterated greedy (IG) algorithm is introduced into the mutation operator to obtain a promising solution in exploitation phase. A variable neighbor search strategy, which is based on the block neighborhood structure and the insert neighborhood structure, is designed to perform the local search around the current best solution in each generation. Furthermore, the global convergence performance of the HBV is analyzed with the Markov model. The computational results and comparisons with other state-of-art algorithms based on Taillard and VRF benchmark show that the efficiency and performance of HBV for solving NWFSP. [ABSTRACT FROM AUTHOR]

Highlights • An FPAPSO algorithm is proposed for solving NWFSP. • The factorial representation is employed to map the search space to integer domain. • A VNS is introduced to search around the promising area in each generation. • The PA mechanism is designed to control the diversity of population. • The runtime analysis of FPAPSO is performed with the level-based theorem. Abstract The no-wait flow shop scheduling problem (NWFSP) performs an essential role in the manufacturing industry. In this paper, a factorial based particle swarm optimization with a population adaptation mechanism (FPAPSO) is implemented for solving the NWFSP with the makespan criterion. The nearest neighbor mechanism and NEH method are employed to generate a potential initial population. The factorial representation, which uniquely represents each number as a string of factorial digits, is designed to transfer the permutation domain to the integer domain. A variable neighbor search strategy based on the insert and swap neighborhood structure is introduced to perform a local search around the current best solution. A population adaptation (PA) mechanism is designed to control the diversity of the population and to avoid the particles being trapped into local optima. Furthermore, a runtime analysis of FPAPSO is performed with the level-based theorem. The computational results and comparisons with other state-of-the-art algorithms based on the Reeve's and Taillard's instances demonstrate the efficiency and performance of FPAPSO for solving the NWFSP. [ABSTRACT FROM AUTHOR]

A novel hybrid optimization algorithm combining search area segmentation technique and the fast Fourier transform (HSAS/FFT) is presented to solve the numerical optimization problems. Firstly, the spectrum of each dimension of the objective function can be acquired by the FFT. The search space is segmented by using the spectrum to ensure that each subspace is unimodal. Secondly, the population of subspaces is produced and the optimal individual can be obtained by gradient descent algorithm. Finally, the local optimal solution in the optimal subspace is generated by the binary search algorithm. Make the optimal individual the new search space and repeat the process until meeting the termination condition. The proposed HSAS/FFT was tested on the CEC2017 benchmark, which evaluates the performance of the proposed algorithm on solving global optimization problems. Results obtained show that HSAS/FFT has an excellent performance and better convergence speed in comparison with some of the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Highlights • A two-stage differential biogeography-based optimization (TDBBO) is proposed. • A migration model based on the two-stage mechanism is proposed. • A BBO / current − to − select /1 is designed to alleviate the rotational variant. • Gaussian mutation is employed to enhance the exploration ability. • The convergence performance of TDBBO is analyzed with the Markov model. Abstract Biogeography-based optimization (BBO) has drawn a lot of attention as its outstanding performance. However, same with certain typical swarm optimization algorithm, BBO severely suffers from premature convergence problem and the rotational variance of migration operator. In this paper, a two-stage differential biogeography-based optimization (TDBBO) is proposed to address the premature convergence problem and alleviate the rotational variance. In the migration operator, the emigration model is selected according to the two-stage mechanism. The constant emigration model is employed to maintain the diversity of population in the early evolutionary process. The sinusoidal emigration model is selected to accelerate the convergence speed in the late evolutionary process. Meanwhile, the BBO/current-to-select/1, which is a rotationally invariant arithmetic crossover operator, is designed to alleviate the rotational variance. The standard mutation operator is replaced by the Gaussian mutation operator to jump out the local optimum effectively. The greedy selection strategy is introduced to accelerate the convergence speed after the migration and the mutation operators. Besides, the convergence performance of TDBBO is analyzed with the Markov model. Compared with the standard BBO and other outstanding BBO variants on CEC 2017 benchmarks, the TDBBO is superior to the state-of-art BBO variants in terms of solution quality, convergence speed and stability. The TDBBO lays a solid foundation for solving optimization problems of expert and intelligent systems. [ABSTRACT FROM AUTHOR]

The Gravitational Search Algorithm (GSA) has excellent performance in solving various optimization problems. However, it has been demonstrated that GSA tends to trap into local optima and are easy to lose diversity in the late evolution process. In this paper, a new hybrid algorithm based on self-adaptive Gravitational Search Algorithm (GSA) and Differential Evolution (DE) is proposed for solving single objective optimization, named SGSADE. Firstly, a self-adaptive mechanism based on GSA is proposed for improving the convergence speed and balancing exploration and exploitation. Secondly, the diversity of the population is maintained in the evolution process by using crossover and mutation operation from DE. Besides, to improve the performance of the algorithm, a new perturbation based on Levy flight theory is embedded to enhance exploitation capacity. The simulated results of SGSADE on 2017 CEC benchmark functions show that the SGSADE outperforms the state-of-the-art variant algorithms of the GSA. [ABSTRACT FROM AUTHOR]

The large-scale real value problem in continuous and discrete optimization problems is a challenging issue for researchers and practitioners in the manufacturing domain. An improved artificial bee colony algorithm (ABC) combined with multi-agent reinforcement learning (called MARLABC) is presented for addressing the large-scale real value optimization problem in this study. Two stages including the training and the testing are introduced in the MARLABC via the multi-agent central controller to improve the convergence speed and local exploitation capability of the algorithm. The optimal strategy pool is constructed by training procedures via the multi-agent central controller with Q-learning mechanism. The effective strategy is selected from the optimal strategy pool for each agent during the testing process in the multi-agent central controller. The elite agents in the training population are reserved to generate the testing population to guide the search. The MARLABC algorithm is compared with the ABC variants and state-of-art algorithms on CEC 2017 benchmark problems. The stability and effectiveness of the MARLABC algorithm are confirmed by the experimental results. [ABSTRACT FROM AUTHOR]

A reward function is learned from the expert examples by inverse reinforcement learning (IRL), which is more reliable than an artificial method. The moth–flame optimization algorithm (MFO), which is based on the navigation mechanism of a moth flying at night, has been extensively employed to address the complex optimization problem. An inverse reinforcement learning framework with the Q-learning mechanism (IRLMFO) is designed to strengthen the performance of the MFO algorithm in a large-scale real-parameter optimization problem. The right strategy is chosen by the Q-learning mechanism, using historical data provided by the relevant approach in the strategy pool, which stores strategies that include diverse functions. The competition mechanism is designed to strengthen the exploitation capability of the IRLMFO algorithm. The performance of the IRLMFO is verified on the benchmark test suite in CEC 2017. Experimental results illustrate that the IRLMFO outperforms state-of-the-art algorithms. • An inverse reinforcement learning framework with the Q-learning mechanism is designed. • The framework is applied in the metaheuristic algorithm. • The comparison of IRLMFO with state-of-the-art algorithms is presented. [ABSTRACT FROM AUTHOR]

In this paper, a discrete Water Wave Optimization algorithm (DWWO) is proposed to solve the no-wait flowshop scheduling problem (NWFSP) with respect to the makespan criterion. Inspired by the shallow water wave theory, the original Water Wave Optimization (WWO) is constructed for global optimization problems with propagation, refraction and breaking operators. The operators to adapt to the combinatorial optimization problems are redefined. A dynamic iterated greedy algorithm with a changing removing size is employed as the propagation operator to enhance the exploration ability. In refraction operator, a crossover strategy is employed by DWWO to avoid the algorithm falling into local optima. To improve the exploitation ability of local search, an insertion-based local search scheme which is utilized as breaking operator, is applied to search for a better solution around the current optimal solution. A ruling out inferior solution operator is also introduced to improve the convergence speed. The global convergence performance of the DWWO is analyzed with the Markov model. In addition, the computational results based on well-known benchmarks and statistical performance comparisons are presented. Experimental results demonstrate the effectiveness and efficiency of the proposed DWWO algorithm for solving NWFSP. [ABSTRACT FROM AUTHOR]

The permutation flow shop scheduling problem (PFSSP), one of the most widely studied production scheduling problems, is a typical NP-hard combinatorial optimization problem. In this paper, a hybrid harmony search algorithm with efficient job sequence mapping scheme and variable neighborhood search (VNS), named HHS, is proposed to solve the PFFSP with the objective to minimize the makespan. First of all, to extend the HHS algorithm to solve the PFSSP effectively, an efficient smallest order value (SOV) rule based on random key is introduced to convert continuous harmony vector into a discrete job permutation after fully investigating the effect of different job sequence mapping schemes. Secondly, an effective initialization scheme, which is based on NEH heuristic mechanism combining with chaotic sequence, is employed with the aim of improving the solution’s quality of the initial harmony memory (HM). Thirdly, an opposition-based learning technique in the selection process and the best harmony (best individual) in the pitch adjustment process are made full use of to accelerate convergence performances and improve solution accuracy. Meanwhile, the parameter sensitivity is studied to investigate the properties of HHS, and the recommended values of parameters adopted in HHS are presented. Finally, by making use of a novel variable neighborhood search, the efficient insert and swap structures are incorporated into the HHS to adequately emphasize local exploitation ability. Experimental simulations and comparisons on both continuous and combinatorial benchmark problems demonstrate that the HHS algorithm outperforms the standard HS algorithm and other recently proposed efficient algorithms in terms of solution quality and stability. [ABSTRACT FROM AUTHOR]

A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA) is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The distributed production scenario with the sequence-dependent setup times (SDST) widely exists in the modern manufacturing system. This paper investigates the distributed blocking flow-shop scheduling problem with sequence-dependent setup times (SDST/DBFSP). Considering the complexity of the distributed scenario and SDSTs, a discrete heuristic and meta-heuristic is proposed by exploring the problem-specific knowledge. First, a knowledge-incorporated construction heuristic is proposed to reduce the blocking times and idle times generated by SDSTs. In the first stage of the meta-heuristic, an insertion-based neighborhood operator of different factories is developed to explore promising regions in the decision space. In the second stage, a local search operator is embedded to enhance the exploitation ability. Additionally, a simulated annealing-like acceptance criterion of the iterated greedy algorithm is employed to keep the diversity of the population. Finally, an insertion operation for critical factories is introduced to further improve the accuracy of the solutions. Moreover, a speedup method for the insertion neighborhood is expanded to reduce the computational complexity of SDST/DBFSP. In the part of the experiment, a deconstruction process is designed to gain insight into the contribution of each component in the proposed meta-heuristic. The proposed meta-heuristic is assessed through comparing with five state-of-the-art algorithms to demonstrate its effectiveness. The experimental results testified that the proposed meta-heuristic outperforms other algorithms regarding the significance of the SDST/DBFSP. [ABSTRACT FROM AUTHOR]

The distributed assembly blocking flow shop scheduling problem, which is a significant scenario in modern supply chains and manufacturing systems, has attracted significant attention from researchers and practitioners. To formulate the problem, a mixed-integer linear programming model is introduced to optimize the total flowtime. A constructive heuristic (HHNRa) and a self-learning hyper-heuristic (SLHH) are proposed to address the scheduling problem. HHNRa is designed based on the problem-specific knowledge to obtain initial solutions with high quality. A self-learning high-level strategy based on the historical success rate of low-level heuristics is presented to manipulate the low-level heuristics to operate in the solution space. In addition, a restart scheme with three distinct constructive heuristics is utilized to maintain the diversity of the solution. Based on 900 small-scale benchmark instances and 810 large-scale benchmark instances, comprehensive numerical experiments are conducted to evaluate the performance of the proposed SLHH algorithm. The results of the statistical analysis indicate that the proposed self-learning hyper-heuristic is superior to the compared state-of-the-art algorithms for the problem under consideration. Consequently, the proposed constructive heuristic and the self-learning hyper-heuristic are effective methods for the distributed assembly blocking flow shop scheduling problem. • A self-learning hyper-heuristic is proposed to solve the DABFSP. • The characteristics of the DABFSP is extracted to construct an initial solution. • The historical information of LLH is utilized to guide the selection of the next LLH. • The restart scheme is introduced to keep the diversity of solutions. • The experimental results demonstrated the validity of the SLHH. [ABSTRACT FROM AUTHOR]

• A novel hierarchical learning scatter search algorithm (TCSSMH) is proposed. • Multi-swarm hierarchical learning mechanism is introduced into SS to update Refset. • A cooperative interactive learning mechanism is introduced to update individuals. • A superior performance of the proposed approaches is proved in the experiments. Scatter search (SS) is a population-based metaheuristic algorithm, which has been proved high efficiency and effective optimizer for complex continuous real value problems. A two-stage cooperative SS guided with the multi-population hierarchical learning mechanism (TCSSMH) to overcome the slow convergence speed of the original SS is proposed. Three strategies are applied to the original SS. Firstly, TCSSMH adopts an adaptive two-way selection search strategy based on the elite reference set (RefSet), which is elite-oriented and ensures the quality of the population. Secondly, the multi-group hierarchical learning mechanism is embedded in the updating process of the RefSet , and the population of the candidates is divided into three levels including excellent candidates, medium candidates, and inferior candidates according to the fitness value of the function. These three subpopulations cooperate to balance the exploration and exploitation ability of the algorithm in the process of evolution. Finally, each subpopulation adopts an interactive learning strategy to increase the diversity of the population and avoid premature convergence of solutions. The optimum of each subpopulation with high accuracy is obtained by the pattern search (PS) optimization. The stronger search ability and higher search efficiency of these additional proposed strategies are verified by extensive experiments. The TCSSMH algorithm is tested on the CEC2017 benchmark test suite and practical engineering problems. The experimental results show that the TCSSMH algorithm is superior to other state-of-the-art algorithms in global search ability and convergence on the benchmark problems. [ABSTRACT FROM AUTHOR]

• A distributed no-wait flow shop scheduling problem with due windows is proposed. • A discrete knowledge-guided learning fruit fly optimization is designed. • The KNEH dw based on no-wait problem property knowledge is introduced. • A probability knowledge model with learning and feedback is designed. • The variable neighborhood descent (VND) strategy is employed. The distributed no-wait flow shop scheduling problem with due windows (DNWFSPDW) is a novel and considerable model for modern production chain and large manufacturing industry. The object of total weighted earliness and tardiness (TWET dw) is a common cost indicator in application. A discrete knowledge-guided learning fruit fly optimization algorithm (DKLFOA) is proposed in this study to minimize TWET in DNWFSPDW. A knowledge-based structural initialization method (KNEH dw) is proposed to construct an effective initial solution. In the KNEH dw , the property that the job has no waiting time between processing machines in the no-wait flow shop scheduling problem is abstracted as knowledge to instruct jobs to be placed in possible positions. The swarm center expands from a single individual to an elitist swarm in the vision search stage. A probability knowledge model is established based on the sequence relationship of jobs in the elite population. The feedback information in the iterative process using the probabilistic knowledge model leads the population to search in the direction with a high success rate. The inferior individuals are allocated to the corresponding elite individuals for the local search in the olfactory search stage. The knowledge of weight in due windows is utilized to avoid invalid search during the iteration process. The variable neighborhood descent (VND) strategy is adopted in the local search to enhance the accuracy of the proposed algorithm and jump out of the local optimal. The design of experimental method (DOE) is introduced to calibrate the parameters in the algorithm. The simulation results show that DKLFOA has advantages for solving DNWFSPDW problems comparing with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Harmony Search (HS) algorithm is a new population-based meta-heuristic which imitates the music improvisation process and has been successfully applied to a variety of combination optimization problems. In this paper, a self-adaptive harmony particle swarm optimization search algorithm, named SHPSOS, is proposed to solve global continuous optimization problems. Firstly, an efficient initialization scheme based on the PSO algorithm is presented for improving the solution quality of the initial harmony memory (HM). Secondly, a new self-adaptive adjusting scheme for pitch adjusting rate (PAR) and distance bandwidth (BW), which can balance fast convergence and large diversity during the improvisation step, are designed. PAR is dynamically adapted by symmetrical sigmoid curve, and BW is dynamically adjusted by the median of the harmony vector at each generation. Meanwhile, a new effective improvisation scheme based on differential evolution and the best harmony (best individual) is developed to accelerate convergence performance and to improve solution accuracy. Besides, Gaussian mutation strategy is presented and embedded in the SHPSOS algorithm to reinforce the robustness and avoid premature convergence in the evolution process of candidates. Finally, the global convergence performance of the SHPSOS is analyzed with the Markov model to testify the stability of algorithm. Experimental results on thirty-two standard benchmark functions demonstrate that SHPSOS outperforms original HS and the other related algorithms in terms of the solution quality and the stability. [ABSTRACT FROM AUTHOR]

This paper designed a multipopulation cooperative coevolutionary framework with a two-stage orthogonal learning (OL) mechanism for the whale optimization algorithm (MCCWOA) to improve the performance of the whale optimization algorithm (WOA). In the framework, a prediction model of the neighborhood structure is established by discovering the guidance information of the following iteration process in the objective space at the first-stage OL. In the second-stage OL, an auxiliary vector pool with various features in the decision space is introduced to guide the candidates falling in the stagnant status to conduct more valuable exploration. According to the domain knowledge of the candidates, the population is divided into the elite population, the intermediate population, and the inferior population. The information of the subpopulations has interacted with the corresponding historical populations in the evolution processes to enhance the ability of cooperative coevolution among individuals. A standard set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed algorithm. The results of the statistical analysis, diversity analysis, and convergence analysis testified that the MCCWOA outperforms the 15 state-of-the-art algorithms regarding efficiency and significance. [ABSTRACT FROM AUTHOR]

The distributed assembly blocking flow-shop scheduling problem (DABFSP), which is a promising area in modern supply chains and manufacturing systems, has attracted great attention from researchers and practitioners. However, minimizing the total tardiness in DABFSP has not captured much attention so far. For solving the DABFSP with the total tardiness criterion, a mixed integer linear programming method is utilized to model the problem, wherein the total tardiness during the production process and assembly process are optimized simultaneously. A constructive heuristic (KBNEH) and a water wave optimization algorithm with problem-specific knowledge (KWWO) are presented. KBNEH is designed by combining a new dispatching rule with an insertion-based improvement procedure to obtain solutions with high quality. In KWWO, effective technologies, such as the re-developed destruction–construction​ operator, four local search methods under the framework of the variable neighborhood search strategy (VNS), the path-relinking method are applied to improve the performance of the algorithm. Comprehensive numerical experiments based on 900 small-scale benchmark instances and 810 large-scale benchmark instances are conducted to evaluate the performance of the presented algorithm. The experimental results obtained by KWWO are 1 to 4 times better than those obtained by the other comparison algorithms, which demonstrate that the effectiveness of KWWO is superior to the compared state-of-the-art algorithms for the considered problem. • Total tardiness minimization for the distributed assembly blocking flow-shop is investigated. • A constructive heuristic is proposed to obtain competitive initial solutions. • An effective water wave optimization algorithm with problem-specific knowledge is proposed. • Experimental results show the effectiveness of the presented methods. [ABSTRACT FROM AUTHOR]

An improved Jaya algorithm named surrogate-assisted Jaya algorithm based on optimal directional guidance and historical learning mechanism (SDH-Jaya) is proposed in this study to address the continuous optimization problems. In the SDH-Jaya, a surrogate-assisted model combined with the polynomial model and radial basis model built by the individual with real fitness is introduced to decrease the expensive computational simulations and accelerate the convergence speed. Two co-evolutionary mechanisms, which are named assisted co-evolutionary mechanism and self-learning co-evolutionary mechanism, are proposed to optimize the surrogate model and evolutionary population. Search directions and steps of the SDH-Jaya are adjusted adaptively by the differential vector resulting from the best solution and worst solution in the candidates at each generation. The historical population stored in an archive is selected randomly to provide new search areas for improving the diversity of the population during the evolution process of the SDH-Jaya. The performance of SDH-Jaya is tested on CEC2017 benchmark problems. The experimental results reveal that the effectiveness of the SDH-Jaya algorithm outperforms the classical Jaya algorithm, its variants, and state-of-the-art algorithms in terms of the quality of solution and execution time. • The SDH-Jaya based on optimal directional guidance and historical learning is proposed. • A surrogate-assisted model is introduced in the SDH-Jaya. • The co-evolutionary and self-learning mechanism are incorporated in the surrogate model. • The differential vector is utilized to adjust the search directions and steps. • The historical population stored in the archive is adopted to generate new search areas. [ABSTRACT FROM AUTHOR]

The job shop problem is an important part of scheduling in the manufacturing industry. A new intelligent algorithm named Shuffled Complex Evolution (SCE) algorithm is proposed in this paper with the aim of getting the minimized makespan. The sequence mapping mechanism is used to change the variables in the continuous domain to discrete variables in the combinational optimization problem; the sequence, which is based on job permutation, is adopted for encoding mechanism and sequence insertion mechanism for decoding. While considering that the basic SCE algorithm has the drawbacks of poor solution and lower rate of convergence, a new strategy is used to change the individual’s evolution in the basic SCE algorithm. The strategy makes the new individual closer to best individual in the current population. The improved SCE algorithm (ISCE) was used to solve the typical job shop problems and the results show that the improved algorithm is effective to the job shop scheduling. [ABSTRACT FROM AUTHOR]

Brain storm optimization algorithm (BSO), which is inspired by brain storm process of human, has been adopted as an efficient optimizer for various complex problems. A reinforcement learning brain storm optimization algorithm (RLBSO) to improve the performance of BSO is proposed in this paper. Four mutation strategies are designed to enhance the search capability of the algorithm in different stages. Elites are adopted as the guidance to ensure the quality of the population. The global best is utilized to guide the search direction of individuals. The cluster centers are employed to improve the exploitation ability of the algorithm. The historical individuals are utilized to the increase the diversity of the population. The Q-learning mechanism is introduced to guide the selection of strategies according to the historical information fed back by the corresponding strategies. A self-learning mechanism, which is based on the evolutionary state of the population and the experience of previous successful individuals, is utilized to determine the update method of individual. The RLBSO algorithm is tested on the CEC 2017 benchmark test suite and a practical engineering problem. The results show that RLBSO has better performance than the other state of the arts algorithms. • A reinforcement learning brain storm optimization algorithm (RLBSO) is proposed. • Four mutation strategies are introduced to increase the ability of global search. • Q-learning is utilized to guide the algorithm to the appropriate evolution strategy. • A self-learning strategy is utilized to determine the update method of the individual. • The RLBSO algorithm is tested on the CEC 2017 benchmark test suite. [ABSTRACT FROM AUTHOR]

• A novel cooperative learning fruit fly optimization algorithm (HGCLFOA) is proposed. • The hierarchical guidance strategy for local search is implemented in the olfactory search. • The inferior solution repairing (ISR) strategy is employed to modify the search direction. • The hybrid GEDA with previous information is applied to guide the evolution in vision search. • A probability selection strategy based on feedback of objective space is introduced. The fruit fly optimization algorithm (FOA) has drawn enormous attention from researchers and practitioners in the computation intelligence domain for the benefits of simple implementation mechanism and few parameters tuning requirement of FOA. However, FOA is hard to adapt directly to address complex continuous problems. A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism (HGCLFOA) is proposed in this study. The population is divided into elitist and inferior subpopulations with the fitness of objective function. The population center is re-designed as an elitist subpopulation to maintain the diversity of the population. In the olfaction search stage, the hierarchical guidance strategy is introduced for local search according to the difference of solution qualities to assign inferior individuals to elitist individuals on different levels. Meanwhile, the inferior information is applied by the inferior solutions repairing strategy to deflect the prediction of the elitist subpopulation for preventing HGCLFOA from falling into the local optimum. In the vision search stage, a hybrid Gaussian distribution estimation strategy is adopted to extract the elitist information of previous generations to predict the distribution of potential elitist individuals in the next generation. The exploration and exploitation of the HGCLFOA are balanced by the cooperation between elitist subpopulation and inferior subpopulation. A random walk strategy is activated to assist the elitist solutions to jump out the local optimal. The parameters of the HGCLFOA are calibrated by DOE and ANOVA methods. The experimental results demonstrated that the HGCLFOA outperformed the classical FOA and state-of-arts variants of FOA. [ABSTRACT FROM AUTHOR]

Virtual enterprise is a basic organization form to achieve agile manufacturing in a manufacturing enterprise. One of the key factors of virtual enterprise’s success is that the dominant enterprise can make the correct decision in the selection of a cooperative partner. In this paper, a multi-objective optimization model is proposed from an activity network project. Based on the concept of inefficient candidate, the solution space of the problem is first reduced. Then, an R-GA with embedded project scheduling is developed for solving the problem, where fuzzy factors-based rules are proposed in order to modify the partner selection according to different situations in the evaluation process of the genetic algorithm by using the characteristics of the considered problem and the knowledge of project scheduling. The results indicate that the proposed model and algorithm can obtain satisfactory solutions. [ABSTRACT FROM AUTHOR]

To improve the performance of the backtracking search optimization algorithm (BSA), a multi-population cooperative evolution strategy guided BSA with hierarchical knowledge (HKBSA) is proposed in this paper. According to the domain knowledge of the candidates in objective space, the population is divided into the dominant population, the ordinary population and the inferior population. The information between the sub-populations has interacted with the evolution processes of the three sub-populations. The individuals in the dominant population are maintained as the optimal solutions and are utilized to guide the evolution of the other two sub-populations. A multi-strategy mutation mechanism is applied to solve non-separable problems. The distribution vector of inferior individuals is constructed by sampling, and a mechanism of the individual generation with feedback is proposed by combining self-learning strategy and elite learning strategy. The convergence of HKBSA is analyzed with the Markov model. Compared with the state-of-the-art BSA variants, HKBSA outperforms other algorithms in terms of the speed of convergence, solution accuracy and stability. • A Hierarchical Knowledge Guided Backtracking Search Algorithm (HKBSA) is proposed. • The population of HKBSA is divided into the dominant, ordinary and inferior population. • A multi-strategy mutation mechanism is applied to solve non-separable problems. • The self-learning and elite learning strategy are introduced to individual generation. • The convergence of HKBSA is analyzed with the Markov model. [ABSTRACT FROM AUTHOR]

• An ensemble discrete differential evolution (EDE) algorithm is proposed. • Two different heuristic methods and one random strategy are introduced into EDE. • The mutation, crossover and selection operators are redesigned to assist the EDE algorithm. • An elitist retain strategy is introduced into the framework of EDE algorithm. • The parameters of the EDE algorithm are calibrated by the design of experiments (DOE) method. The distributed blocking flowshop scheduling problem (DBFSP) plays an essential role in the manufacturing industry and has been proven to be as a strongly NP-hard problem. In this paper, an ensemble discrete differential evolution (EDE) algorithm is proposed to solve the blocking flowshop scheduling problem with the minimization of the makespan in the distributed manufacturing environment. In the EDE algorithm, the candidates are represented as discrete job permutations. Two heuristics method and one random strategy are integrated to provide a set of desirable initial solution for the distributed environment. The front delay, blocking time and idle time are considered in these heuristics methods. The mutation, crossover and selection operators are redesigned to assist the EDE algorithm to execute in the discrete domain. Meanwhile, an elitist retain strategy is introduced into the framework of EDE algorithm to balance the exploitation and exploration ability of the EDE algorithm. The parameters of the EDE algorithm are calibrated by the design of experiments (DOE) method. The computational results and comparisons demonstrated the efficiency and effectiveness of the EDE algorithm for the distributed blocking flowshop scheduling problem. [ABSTRACT FROM AUTHOR]

• A hybrid discrete water wave optimization algorithm is proposed to solve NIFSP. • The new initialize method is applied to generate desirable solutions. • Three different strategies are introduced to improve the performance of HWWO. • The control parameters are calibrated with extensive experiments. • The comparisons of HWWO with state-of-the-arts algorithms are presented. The no-idle flowshop has attracted enormous attention owing to its widespread application in the manufacturing industry domain. In this paper, a hybrid discrete water wave optimization algorithm, named HWWO, is presented to solve the NIFSP with total tardiness. In order to improve the quality of a population, an initialize method based on a new priority rule combined with the modified NEH method is proposed to generate a population. In the propagation phase, a self-adaption selection neighborhood search structure is introduced to amplify the search range of waves and balance the exploration and exploitation ability of the HWWO. Afterwards, a variable neighborhood search is adopted to strengthen the local search and maintain the diversity of the population in the breaking phase. In the refraction operation, a perturbation sequence is generated and combined with the local optimal solution found by the breaking operation, in order to generate a new solution, and prevent the algorithm from becoming trapped in the local optimum. Furthermore, the control parameters and time complexity are analyzed. The experimental results and comparisons with the other state-of-the-art algorithms evaluated on Taillard's and Ruiz's benchmark sets reveal that the effectiveness and efficiency of the HWWO outperformed the compared algorithms for solving the NIFSP. [ABSTRACT FROM AUTHOR]

With the continuous development of organic semiconductor materials and on‐going improvement of device technology, the power conversion efficiencies (PCEs) of organic solar cells (OSCs) have surpassed the threshold of 19%. Now, the low production cost of organic photovoltaic materials and devices have become an imperative demand for its practical application and future commercialization. Herein, the feasibility of simplified synthesis for cost‐effective small‐molecule acceptors via end‐cap isomeric engineering is demonstrated, and two constitutional isomers, BTP‐m‐4Cl and BTP‐o‐4Cl, are synthesized and compared in parallel. These two non‐fullerene acceptors (NFAs) have very similar optoelectronic properties but nonuniform morphological and crystallographic characteristics. Consequently, the OSCs composed of PM6:BTP‐m‐4Cl realize PCE of 17.2%, higher than that of the OSCs with PM6:BTP‐o‐4Cl (≈16%). When ternary OSCs are fabricated with PM6:BTP‐m‐4Cl:BTP‐o‐4Cl, the averaged PCE value reaches 17.95%, presenting outstanding photovoltaic performance. Most excitingly, the figure of merit (FOM) values of PM6:BTP‐m‐4Cl, PM6:BTP‐o‐4Cl, and PM6:BTP‐m‐4Cl:BTP‐o‐4Cl based devices are 0.190, 0.178, and 0.202 respectively. The FOM values of these systems are all among the top ones of the current high‐efficiency OSC systems, revealing high cost‐effectiveness of the two NFAs. This work provides a general but accessible strategy to minimize the efficiency‐cost gap and promises the economic prospects of OSCs. [ABSTRACT FROM AUTHOR]

• The proposed algorithm is effective for solving many-objective problems. • Shift-based estimation and sum of objectives can reserve the excellent solutions. • An angle-based selection strategy can improve the diversity of solutions. • Experimental results have been presented by using Wilcoxon's rank-sum test. Due to the curse of dimensionality, the existing evolutionary algorithms have difficulties in balancing convergence and diversity in many-objective problems. To address this shortcoming, this paper proposes an efficient many-objective optimizer named MaOEA-ASS. In the MaOEA-ASS, the angle-based selection strategy is used to obtain solutions with good diversity from the population. In addition, the combination of the shift-based density estimation and the sum of objectives, which uses the iteration information and emphasis the distribution of solutions, is employed to obtain the high-quality solutions approximating the optimal Pareto solutions. The proposed MaOEA-ASS is compared with eight state-of-the-art many-objective optimization algorithms (MaOEAs) on the DTLZ and WFG test suites, and its performance is verified on a practical many-objective problem. The experimental results demonstrate that the proposed MaOEA-ASS has a superior performance over the peer competitors on all considered many-objective problems. [ABSTRACT FROM AUTHOR]

Background: Numerous studies suggest that interpersonal relationships and social support influence the development of postpartum depression and sleep quality for women. However, the effect of support from the husband or the mother-in-law has not been thoroughly validated. The current study examined the relative contribution of marital satisfaction, perceived caring of the mother-in-law, and social support on postpartum depression and sleep quality simultaneously in a path model.Methods: A cross-sectional study was conducted from March to June 2017 in Hebei, China, using a self-report questionnaire. A total of 817 women participated at 6 weeks postpartum. Sociodemographics and information about marital satisfaction, perceived caring of the mother-in-law, social support, postpartum depression, and sleep were collected. Path analysis was used to analyze the cross-sectional data.Results: The final model had a highly satisfactory fit. Marital satisfaction and perceived caring of mother-in-law had both direct and indirect effects on postpartum depression through social support, but these two variables had only an indirect effect on sleep quality through social support and postpartum depression. Sleep quality is a consequence of postpartum depression at 6 weeks after delivery. The prevalence of minor and major postpartum depressive symptoms at 6 weeks postpartum was 41.49 and 23.13%, respectively. A total of 371 (45.41%) women experienced sleep disturbance at 6 weeks postpartum.Conclusions: These findings suggest that interpersonal relationships with family members play important roles in postpartum depression and sleep quality through social support in Chinese women. Improving the relationship between new mothers and their husbands or mothers-in-law and then enhancing social support might reduce postpartum depression and sleep disturbance. [ABSTRACT FROM AUTHOR]

• A two-stage evolution strategy is proposed for solving multi-objective problem. • The convergence and diversity should be balanced in multi-objective optimization. • A local searching method can improve the diversity of solutions in the space. • Experimental results have been presented by using statistical method. The balance of convergence and diversity plays a significant role to the performance of multi-objective evolutionary algorithms (MOEAs). The MOEA/D is a very popular multi-objective optimization algorithm and has been used to solve various real world problems. Like many other algorithms, the MOEA/D also has insufficient ability of convergence and diversity when tackling certain complex multi-objective optimization problems (MOPs). In this paper, a novel algorithm named MOEA/D-TS is proposed for effectively solving MOPs. The new algorithm adopts two stages evolution strategies, the first stage is focused on pushing the solutions into the area of the Pareto front and speeding up its convergence ability, after that, the second stage conducts in the operating solution's diversity and makes the solutions distributed uniformly. The performance of MOEA/D-TS is validated in the ZDT, DTLZ and IMOP problems. Compared with others popular and variants algorithms, the experimental results demonstrate that the proposed algorithm has advantage over other algorithms with regard to the convergence and diversity in most of the tested problems. [ABSTRACT FROM AUTHOR]

Abstract Due to the excessive pursuit of crop yields and the abuse of herbicides, water pollution caused by atrazine (ATR) has become one of the most severe environmental issues threatening the health of fish and aquatic animals. However, no detailed report has been conducted on the mechanisms of ATR immunotoxicity in fish neutrophils. To investigate these mechanism, we exposed peripheral blood neutrophils to 25 μg/ml atrazine for 1, 2, and 3 h. The results showed that ATR induced the mRNA expression of CYPs enzymes (CYP1A1, CYP1B1, CYP1C and CYP3A138), which increased the ROS levels, and inhibited the SOD and CAT activities, GSH content and spurred the accumulation of MDA. Additionally, a significant decline in the OXPHOS, Na+-K+-ATPase and Ca2+-Mg2+-ATPase activities of mitochondria was observed after ATR exposure. Concurrently, ATR activated Caspase3 and induced apoptosis by changing the expression of mitochondrial pathway factors (Bcl-2, BAX, Caspase9) and death receptor pathway major genes (TNF-α, TNFR, Fas, FasL, and Caspase8). The results reported here indicate that the oxidative stress and mitochondrial damage caused by ATR metabolism may play a crucial role in the apoptosis of carp neutrophils, and enrich the immunotoxicological mechanisms of ATR observed in fish. Graphical abstract Image 1 Highlights • Atrazine cause apoptosis in carp neutrophils. • Atrazine damages mitochondrial morphology and function. • Atrazine activates CYP450s and induce oxidative stress. [ABSTRACT FROM AUTHOR]

• Isomeric acceptors compositions control donor–acceptor miscibility via surface energy. • Low miscibility affects film crystallinity, leading to 18.1% efficiency. • Ternary cells with lower energy disorder and recombination outperform binary peers. The donor–acceptor miscibility is one of the most critical factors in controlling the active layer morphologies with multiple components. However, how to practically select the third component with the desired miscibility for efficient ternary organic solar cells remains unresolved. Herein, we demonstrate that the surface energy of thin films could be manipulated by altering the weight ratios of the isomeric acceptors in the blend films and, therefore, the regulation of donor–acceptor miscibility. When the acceptor weight ratio (M−Cl:O-Cl) is 1:0.3, low miscibility (χ = 5.43 × 10-2K) with PM6 is achieved, and the power conversion efficiency of organic solar cells composed of PM6:M−Cl (17.1%) is enhanced to 18.1% (PM6:M−Cl:O-Cl). The increased power conversion efficiency resulted from the surface energy-driven active layer morphology and donor–acceptor phase separations, associated with the significantly lower energetic disorder, trap density, and thus reduced free charge recombination. Our findings underline the significance of the isomeric approach to managing the blend film surface energy and, therefore, the donor–acceptor miscibility for improving the device performance, providing guidelines for the fabrication of high-performed ternary OSCs and material designs. [ABSTRACT FROM AUTHOR]

Experience replay has been widely used in deep reinforcement learning. The learning algorithm allows online reinforcement learning agents to remember and reuse experiences from the past. In order to further improve the sampling efficiency for experience replay, the most useful experiences are expected to be sampled with higher frequency. Existing methods usually designed their sampling strategy according to a few criteria, but they tended to combine different criteria in a linear or fixed manner, where the strategy were static and independent of the agent learner. This ignores the dynamic attribute of the environment and thus can only lead to a suboptimal performance. In this work, we propose a dynamic experience replay strategy according to the interaction between the agent and environment, which is called Prioritized Experience Replay based on Multi-armed Bandit (PERMAB). PERMAB can adaptively combine multiple priority criteria to measure the importance of the experience. In particular, the weight of each assessing criterion can be adaptively adjusted from episode to episode according to their respective contribution to the agent performance, which guarantees useful criterion to be weighted more in its current state. The proposed replay strategy is able to take both sample informativeness and diversity into consideration, which could significantly boosts learning ability and speed of the game agent. Experimental results show that PERMAB accelerates the network learning and achieves a better performance compared to baseline algorithms on seven benchmark environments with various difficulties. [ABSTRACT FROM AUTHOR]

Highlights: [•] HSPs of common carp were investigated by ATR, CPF and their mixture. [•] Our aim was to evaluate the sub-chronic effects of ATR and CPF on carp. [•] In our study, the level of HSPs altered significantly in most of treated groups. [Copyright &y& Elsevier]