High-quality Offspring Research Articles

Pre-Embryonic Period Observation Shows a Unique Reproductive Strategy of the Critically Endangered Anji Salamander (Hynobius amjiensis).

Hynobius amjiensis, also known as the Anji salamander, is an amphibian species currently categorized as endangered due to its limited geographical distribution, primarily in China. To address the critical conservation status of this species, artificial breeding is essential for population expansion. However, progress in artificial breeding efforts has been hindered by the scarcity of research on the reproductive biology of the Anji salamander. In this study, we identified 25 distinct early stages of embryo development. Additionally, we observed that Anji salamander embryos contain a lesser amount of yolk compared to other salamanders or frogs. We further discovered that the Anji salamander employs a highly competitive reproductive strategy, producing a smaller number of high-quality offspring. This strategy aims to generate adaptive individuals through intense intraspecific competition, driven by three factors: extremely confined breeding habitats, a substantial number of eggs, and a reduced yolk content. We introduce the term "mass escape" to describe this strategy, which provides a novel perspective on cannibalization, focusing on the consumption of specific body parts rather than a single-individual activity. This study offers valuable insights into artificial breeding techniques designed to mitigate inherent intraspecific competitive pressure, thereby improving metamorphosis and survival rates. Additionally, it provides a comprehensive table detailing the pre-embryonic developmental stages of the Anji salamander.

Multi-objective evolutionary multi-tasking band selection algorithm for hyperspectral image classification

Hyperspectral images (HSI) contain a great number of bands, which enable better characterization of features. However, the huge dimension and information volume brought by the abundant bands may give rise to a negative influence on the efficiency of subsequent processing on hyperspectral images. Band selection (BS) is a commonly adopted to reduce the dimension of HSIs. Different from the previous work, in this paper, hyperspectral band selection problem is formulated as a multi-objective optimization problem, where the band distribution uniformity among the selected bands and inter-class separation distance from a few labeled samples are optimized simultaneously. To fully exploit the relation between the band subsets with different sizes, we construct a multi-objective evolutionary multi-tasking algorithm for hyperspectral band selection (namely MEMT-HBS) to achieve the selected band subsets for all the selected band sizes in one run. To implement MEMT-HBS, the intra-task pairwise learning based solution generation strategy is suggested to evolve the population for each task to achieve high-quality offspring whose selected band size is restricted to a fixed scope. The inter-task band coverage based knowledge transferring strategy is utilized to choose useful individuals from adjacent tasks to further enhance the performance of current task. Compared with the state-of-the-art semi-supervised and unsupervised BS algorithms, empirical results on different standard hyperspectral datasets show that our proposed MEMT-HBS can determine the superior band subset which has a higher image classification accuracy over the comparison algorithms.

A multidimensional probabilistic model based evolutionary algorithm for the energy-efficient distributed flexible job-shop scheduling problem

With escalating environmental effects, the spotlight on low-carbon manufacturing has garnered significant attention. The rise of distributed production has emerged as a prominent trend in response to the imperatives of economic globalization. This article focuses on addressing the energy-efficient distributed flexible job-shop scheduling problem (EE_DFJSP), with the aim of minimizing both makespan and total energy consumption (TEC) simultaneously. The production process contains four pivotal phases: 1) job assignment in distributed factories; 2) machine selection within factories; 3) operation allocation on flexible machines; and 4) machine speed adjustment for processing. Given the problem's multi-phase and strong coupling characteristics, it is imperative to develop a promising evolutionary algorithm (EA) for EE_DFJSP. To tackle this challenge, we propose a multidimensional probabilistic model-based EA (MPMEA) paradigm. First, problem-specific encoding and decoding schemes are developed based on the solution features of EE_DFJSP. Second, a hybrid initialization strategy incorporating four heuristic rules is devised to yield an initial population with diversity. Third, an effective union probabilistic model (UPM) is formulated to learn promising patterns from superior solutions, and an efficient sampling strategy is designed to produce high-quality offspring individuals. To achieve a balance between global exploration and local exploitation, problem-specific multiple neighborhood operators are proposed to perform an in-depth local search. Furthermore, a two-stage energy-saving speed adjustment strategy is designed for the superior solutions obtained through local search. Finally, computational comparisons and simulation studies are conducted to validate the effectiveness and superiority of the MPMEA in effectively addressing EE_DFJSP.

Effect of Curcuma longa maceration treatment on ovarian follicular development, serum oestradiol, uterine growth and vascularisation in female albino rats.

Curcuma longa is a well-known medicinal plant with various health benefits. This study was designed to evaluate the administration of Indonesian C. longa maceration for its effect on promoting growth and development of the ovary and uterus before mating in female albino rats. A total of 15 female Sprague Dawley rats in their dioestrous phase were assigned into three different groups: the Control group (mineral water); the Cur-Low group (mineral water with 1% C. longa maceration) and the Cur-High group (mineral water with 5% C. longa maceration). The treatments were given for 20 days. Serum concentrations of follicle-stimulating hormone, oestradiol and progesterone were determined. After the sacrifice of the rats, ovary and uterine relative weight, uterine cornua diameter and length, uterine gland diameter (by histology), the number of primary, secondary, tertiary, and Graafian follicles, the number of corpora lutea and vascular endothelial growth factor (VEGF) expression in the ovary were measured. Uterine vascularisation was also evaluated. Administration of C. longa maceration significantly improved the relative weights of the uterus and ovary; uterine cornua diameter, length and vascularisation; uterine gland diameter; and expression of VEGF in the ovary. It also increased the number of tertiary follicles and corpora lutea, albeit not significantly. Follicle-stimulating hormone serum concentrations were lower in the administered rats. Oestradiol and progesterone levels rose with C. longa maceration treatment. The maceration improved the reproductive organs of unmated rats and had potential to optimise the uterine environment for supporting pregnancy in order to produce high-quality offspring.

Relationship between acoustic traits of protesting cries of domestic kittens (Felis catus) and their individual chances for survival

Domestic cat (Felis catus) mothers may rely on offspring cries to allocate resources in use of individuals with greater chances for survival and sacrifice the weak ones in case of impossibility to raise the entire large litter. Potential victims of this maternal strategy can enhance their chances of survival, by producing vocalizations with traits mimicking those of higher-quality offspring. We compared acoustic traits of 4990 cries produced during blood sampling by 57 two-week-old captive feral kittens (28 males, 29 females); 47 of them survived to 90 days of age and 10 died by reasons not related to traumas or aggression. No relationship was found between acoustic parameters and kitten survival, however, positive relationship was found between survival and body weight. The cries had moderate cues to individuality and lacked cues to sex. Body weight correlated positively with fundamental frequency and negatively with call rate, duration, peak frequency and power quartiles. We discuss that dishonesty of acoustic traits of kitten quality could develop as adaptation for misleading a mother from allocation resources between the weaker and stronger individuals, thus enhancing individual chances for survival for the weaker littermates. Physical constraint, as body weight, may prevent extensive developing the deceptive vocal traits.

Dual-Fuzzy-Classifier-Based Evolutionary Algorithm for Expensive Multiobjective Optimization

Multiobjective evolutionary algorithms (MOEAs) have been widely used to solve multiobjective optimization problems (MOPs). Conventional MOEAs usually require a large number of function evaluations (FEs) for evaluating the quality of solutions. However, only a limited number of FEs are affordable in many real-world optimization problems where the FEs are computationally or economically expensive. The use of a large number of FEs decreases the effectiveness of MOEAs in solving these problems. This article proposes a dual fuzzy classifier-based surrogate model (DFC) and a DFC-based multiobjective evolutionary algorithm framework (DFC-MOEA) for expensive optimization problems. The DFC model is used for offspring pre-selection to choose high-quality offspring solutions and for categorization to divide unevaluated solutions into three categories. In the proposed framework, two fuzzy classifiers are built to predict the quality of each unevaluated solution. To strike a balance between convergence and diversity, one classifier is designed for predicting the dominance relation of unevaluated solutions (i.e., convergence), and another classifier is designed for predicting the crowdedness of unevaluated solutions (i.e., diversity). The integration of the proposed framework in three different types of MOEAs (i.e., a dominance-based MOEA, an indicator-based MOEA, and a decomposition-based MOEA) demonstrates its usefulness in handling expensive optimization problems. Comprehensive experiments on four well-known test suites and several real-world optimization problems demonstrate that the proposed framework is able to improve the performance of MOEAs under a limited number of FEs.

MaOEA/D with adaptive external population guided weight vector adjustment

In order to make multiobjective evolutionary algorithm based on decomposition (MOEA/D) have better performance in dealing with many-objective optimization problems (MaOPs) with different Pareto fronts (PFs), this paper proposes a MOEA/D with adaptive external population guided weight vector adjustment (MaOEA/D-AEW). The algorithm modifies the MOEA/D method of constructing mating pools by calculating the utility function value of individuals in the population to produce high-quality offspring. For the maintenance of non-dominated solutions in the external population (EP), solutions exceeding the capacity of the EP are removed by the method of minimum Euclidean distance combined with the aggregation function value to ensure the convergence and diversity of non-dominated solutions in the EP. For the addition of weight vectors, a new sparsity evaluating method (SL+) is proposed to determine the sparsity of weight vectors by multiplying parallel distances between individuals. For the deletion of the weight vector, the minimum Euclidean distance combined with the aggregation function value is used to delete the weight vector corresponding to invalid or crowded individuals in the population. Comparing MaOEA/D-AEW with six state-of-the-art algorithms on regular PF and irregular PF test problems respectively, simulation results show that the proposed algorithm can adapt well to MaOPs with different PFs and significantly outperforms other comparative algorithms in terms of convergence and diversity of solution sets.

Neuroevolutionary diversity policy search for multi-objective reinforcement learning

Sequential decision-making requires balancing multiple conflicting objectives through multi-objective reinforcement learning (MORL). Moreover, decision-makers desire dense solutions that satisfy their requirements and consider the trade-offs between different objectives (Pareto optimal solutions). Most deep reinforcement learning methods focus on single-objective problems or solve multi-objective problems using simple linear combinations, which may oversimplify the underlying problem and lead to suboptimal results. This study proposes a neuroevolutionary diversity policy search approach to address MORL problems. It employs neural networks, each equipped with a buffer for storing recent experiences, representing individuals in a population. The non-dominated sorting method and diversity distance metric are employed in the evolutionary process to select high-quality solutions as teachers. The teachers use gradient-based genetic operators to guide the population to produce high-quality offspring, thereby achieving dense Pareto optimal solutions. Furthermore, we introduce three MORL benchmarks with distinct characteristics: (1) a continuous deep sea treasure with convex and nonconvex Pareto fronts; (2) a multi-objective mountain car with sparse rewards and a discontinuous Pareto front; and (3) a multi-objective HalfCheetah with high-dimensional action-state spaces. The experimental results on the three MORL benchmarks demonstrate the superiority of the proposed algorithm in obtaining dense and high-quality Pareto optimal solutions.

Directed quick search guided evolutionary framework for large-scale multi-objective optimization problems

For large-scale multi-objective evolutionary algorithms (LSMOEAs), obtaining efficient evolutionary directions in an ultrahigh-dimensional decision space to produce high-quality offspring is a major challenge. This study proposes a novel framework called the directed quick search-guided large-scale evolutionary framework (QSLMOF) to address multi-objective optimization problems on a large scale. The framework contains two innovative strategies: the bidirectional vector-based sampling strategy (BDVS) and the quick search-guided directed reproduction strategy (QS-DRS). BDVS is introduced as an approach to swiftly discern promising solutions that can steer the exploration in the large-scale decision space. This is achieved by formulating two distinct types of sampling directions to rapidly reduce the search space and strike a delicate balance between convergence and diversity. In QS-DRS, we introduced the concept of the potential convergence gradient (PCG), incorporating directional information from historical searches and convergence directions indicated byelite solutions in the current population. With this property, inferior solutions can obtain excellent search directions to explore the decision space, which can improve the convergence rate and prevent the search from falling into a local optimum. The proposed large-scale evolutionary framework incorporates an existing environmental selection mechanism. Comprehensive experiments show that the two novel strategies improve the search efficiency and evolutionary quality of LSMOEAs in ultrahigh-dimensional decision spaces. Moreover, the proposed framework outperformed seven state-of-the-art LSMOEAs for nine large-scale multi-objective benchmark problems LSMOP1-LSMOP9 with up to three objectives and 10000 decision variables.

PSI-A-9 A Comparison in Vaginal Gene Expression and Circulating Hormone Concentration in Pre-Pubertal Gilts Achieving Puberty at Different Ages

Abstract A known predictor of reproductive success in swine breeding herds is age at puberty. Early puberty is associated with improved long-term reproductive performance and more high-quality offspring. The objective of this study was to compare vaginal gene expression in gilts that achieved estrus early (160 to 181 d of age), average (181 to 202 d of age), late (202 to 215 d of age) and anestrus. Pre-pubertal gilts (n = 13) were followed from 70 d of age until first estrus or 214 ± 1 d. Vaginal epithelia was collected using a swabbing method at five time points during reproductive development [70 (on-farm arrival), 100 (mid-folliculogenesis), 130, 160 (start of boar exposure) d of age and standing estrus or end of trial]. At 140 d of age, females received an indwelling titanium catheter for daily blood collection during boar exposure. Following detected standing estrus, a blood sample was collected every 2 hours for 96 hours to characterize the peak serum concentration of luteinizing hormone that occurs at ovulation. At the conclusion of the trial, 3 gilts displayed early estrus (169 to 171 d of age), 3 gilts were deemed average estrus (194 to 195 d of age), 3 gilts were deemed late estrus (203 to 213 d of age), 3 gilts were deemed anestrus and 1 was identified as having a ‘silent’ estrus after 200 d of age. Total RNA were isolated from vaginal epithelia and relative gene expression insulin-like growth factor-1 (IGF-1), and estrogen receptor (ER)-alpha was quantified by real time RT-PCR, relative to the expression of RPLP0. to D70 was preformed using the PCR package offered in RStudio (version 1.2.5025). There was an increase in ER-alpha expression in the average estrus group (P = 0.03) at 130 d of age. Early estrus females had a greater expression of IGF-1 at 130 d of age compared with anestrus females (P = 0.01). Of the 9 females placed with an indwelling catheter, 2 catheters were nonpatent, and blood sample could not be collected at standing estrus. Of the 7 females where blood sample was collected at standing estrus, all females achieved an luteinizing hormone (LH) peak within 12 h after recorded standing estrus. Identification of key differences in the vaginal epithelium in concert with LH values provides biological evidence that those deemed achieving estrus based on behavior did successfully achieve estrus. These vaginal epithelium differences may serve as putative biomarkers to detect early estrus in pre-pubertal gilts.

A constrained multiobjective differential evolution algorithm based on the fusion of two rankings

The tradeoff between objective functions and constraints is a key issue that needs to be addressed by constrained multiobjective optimization algorithms, and constraint handling techniques (CHTs) are an important technique for balancing objective functions and constraints. In this paper, a novel CHT that fuses two rankings is proposed. Specifically, each individual is assigned two rankings: one ranking calculated based on Pareto dominance (regardless of constraints) and another calculated based on the constrained dominance principle (CDP). The fitness value of an individual is the weighted sum of these two rankings, and the weight is related to the generation number and the proportion of feasible solutions in the current generation. Based on the proposed CHT, a constrained multiobjective differential evolution algorithm is proposed. To generate high-quality offspring, the proposed constrained multiobjective differential evolution algorithm combines four mutation operations as core components of the search algorithm. The proposed algorithm is compared with eight state-of-the-art algorithms in experiments with five test suites, and the experimental results show that the proposed algorithm performs significantly better than the eight state-of-the-art algorithms.

Nestling growth rate and food consumption increases under experimentally prolonged daylength in a New World sparrow

When evaluating avian reproduction, life history theory examines the trade‐offs between parental effort, the number and size of offspring, and the rate of nestling development. The growth rates and body sizes of developing birds vary geographically and can diverge with both latitude and migratory strategy. In terms of offspring size, growth rate can deviate in nestlings of the same or similar species due to the correlated influences of weather events, predation pressure, food availability, number of nestmates and parental provisioning. Furthermore, a longer photoperiod for species nesting at higher latitudes increases the duration over which a nestling can be fed each day, and increased nestling provisioning has been positively correlated with growth rate. Whether the amount of time a bird is fed during development drives this variation in growth rate and morphology is unknown. By removing supplemental environmental stressors (e.g. weather, predation) and standardizing feeding rate and environment, we explored the influence of daily duration of nestling provisioning on dark‐eyed junco Junco hyemalis nestlings. We hand‐reared 65 chicks of a sedentary junco subspecies J. h. carolinensis under both their natural photoperiod and the longer photoperiod of a closely related migratory subspecies J. h. hyemalis and compared growth rate, mass, morphology and the amount of food consumed. Average growth rate, fasted mass, wing length and total daily food consumption were all greater in birds hand‐reared under the longer, more northern photoperiod treatment. These findings suggest that increased daily photoperiod at higher latitudes may allow for greater total food provisioning and thus may play a role in the ability of parents in compressed breeding seasons to produce high quality offspring. This points to a trade‐off between provisioning effort and nestling growth rate in lower latitude (shorter photoperiod) populations and points to an important role of developmental plasticity on growth rate and morphology.

Effects of Salinity on Fertilization, Hatching, and Larval Performance of Longfin Smelt Spirinchus thaleichthys

Understanding the spawning and rearing habitats of fishes is critical to effective fisheries management and conservation. Longfin smelt Spirinchus thaleichthys is an imperiled migratory fish that is believed to spawn and rear in habitats of varying salinities; however, optimal conditions for each stage remain unknown. Here, we examined the effects of variation in salinity on egg fertilization, hatch success, and larval growth and survival. Eggs that were fertilized in freshwater (0.4 ppt) exhibited a significantly higher fertilization rate (81%) than those fertilized in brackish water (62% at 5 ppt), with no detectible effects of fish origin or female size. In contrast to fertilization rates, once the eggs were fertilized, their hatching rates were not affected by the fertilization salinity, incubation salinity, nor their interaction; however, hatching success and larval survival both increased with increasing maternal body mass. Larval growth rate appeared to be independent of salinity and maternal size. Taken together, the results indicate that fertilization is possible at a range of salinities, but optimal at lower salinities for longfin smelt; however, embryos and larvae can perform well across a range of salinities. Furthermore, results indicated that larger mothers produced high-quality offspring, a finding that supports the “bigger is better” paradigm in fisheries science and management. These results likely explain, in part, the spawning and rearing behaviors of wild longfin smelt and suggest that the conservation culture program would likely be optimized by utilizing freshwater fertilization and larger females as broodstock.

A direction vector-guided multi-objective evolutionary algorithm for variable linkages problems

Recombination is a key component for the evolutionary algorithm to provide promising offspring solutions. However, conventional recombination operators cannot generate high-quality solutions for variable linkages problems due to the particularity of the Pareto optimal set (PS). To tackle this problem, a two-stage multi-objective evolutionary algorithm based on direction vector guidance (DSMOEA) is proposed in this paper. Firstly, a portion of the population is transformed by the eigenmatrix of the covariance matrix to increase the probability of generating high-quality offspring. Then the representative solutions are selected in the transformed population to create the direction vectors. Under the guidance of the direction vectors, the population rapidly approaches PS and generates promising offspring solutions. Finally, Differential Evolution (DE) is performed for searching globally to increase the diversity of the population. The proposed algorithm is tested on three classes of variable linkages problems with 30, 50, and 100 dimensions to verify its performance. The results show that the algorithm is promising for variable linkages problems.

Genetic characterisation of indigenous duck of Tripura state in India using microsatellite markers.

Exploring genetic variability by microsatellite markers is essential for genetic improvement, preservation of indigenous germplasm and production of high-quality offspring. Lack of information on microsatellite profiling of Indian indigenous ducks (Tripura state) has stoked curiosity in this work. Genomic DNA samples from randomly selected 36 native ducks were analysed at 25 duck-specific microsatellite loci. Alleles were separated through 3.4% MetaPhore™ agarose gel electrophoresis. Allele sizes were determined using Image Lab 6 software of GelDoc™ EZ System. Allelic data were analysed by POPGENE version 1.31. Total 112 alleles were resolved and all the loci were found polymorphic with 2 to 15 alleles across the loci. Average number of allele (Na) was 4.480 ± 0.659. Allele sizes and frequencies ranged from 96 to 357bp and 0.014 to 0.819, respectively. Average heterozygosity of Nei, effective number (Ne) of alleles and Shannon's information index (I) were 0.617 ± 0.036, 3.538 ± 0.527 and 1.184 ± 0.112, respectively. The estimates of Ne were less than the Na at all the loci, indicating prevalence of heterozygosity. Polymorphic information content (PIC) ranged from 0.252 (CAUD020) to 0.911 (CAUD019) with an average of 0.562 ± 0.040. Sixteen loci were moderate to highly polymorphic and informative (PIC ˃ 0.5). Chi-square and G-square statistics revealed Hardy-Weinberg disequilibrium at all the loci. Moderate to high level of polymorphism of the studied microsatellites indicated that these markers might be helpful for genetic characterisation and adoption of appropriate conservation strategies to exploit optimum genetic potentiality of indigenous ducks of Tripura.

Ensemble of selection operators for decomposition-based multi-objective evolutionary optimization

Decomposition-based multi-objective evolutionary algorithms (MOEA/D) have demonstrated competitive performance in solving complicated multi-objective optimization problems. The two mainstream selection operators of MOEA/D for updating populations, i.e., neighborhood-based and best-fitness-based selection operators, perform differently in balancing diversity and convergence. This paper explores the properties of the two mainstream selection operators and designs an ensemble approach, striving to integrate their merits and complement each other’s weaknesses. In this ensemble approach, each selection operator manipulates a population, and the offspring solutions reproduced by any population update two populations concurrently. In this way, high-quality offspring solutions reproduced by one population have opportunities to improve the convergence or diversity of another population. Moreover, this ensemble approach includes a novel adaptive strategy to pick up mating solutions from each population according to their contributions during previous generations. Thus, the population with a higher contribution will receive more evolution opportunities to reproduce solutions with better convergence and diversity. Finally, we compare the proposal with twelve baseline algorithms on 43 test cases from four frequently-used test suites and the real-world application of cloud service compositions. The comparison results demonstrate the superiority of the proposal in balancing diversity and convergence.

An operator pre-selection strategy for multiobjective evolutionary algorithm based on decomposition

Evolutionary algorithms (EAs) are a population-based optimization method that adopts survival-of-the-fittest rules. The performance of EAs can be greatly affected by offspring quality. Many researchers hope to adjust the parameters of evolutionary operators to improve offspring quality, but most current methods are regulated by the feedback model, which does not recognize offspring quality. Instead, it adjusts an operator’s search structure dynamically based on its historical performance. In fact, there is a co-evolutionary effect between different operators. An operator will not always produce offspring with the best quality, so the feedback model only selects the best operator in history to generate offspring, which ignores the capacity for co-evolution. In this paper, we propose an operator pre-selection strategy (OPS). First, to ensure operators’ capacity for co-evolution, we construct an operator pool using five operators. Second, we select the best offspring based on the classification and feedback models. Finally, we evaluate the offspring and include it in the evolution. Compared with the traditional feedback model, OPS directly identifies higher-quality offspring. Even if the historical performance of an operator is poor, as long as it can generate excellent offspring, it is selected. Experimental results demonstrate the effectiveness of OPS.

Incremental learning-inspired mating restriction strategy for Evolutionary Multiobjective Optimization

The prior knowledge from the problem property can boost the evolutionary multiobjective optimization (EMO). The existing machine learning model for knowledge mining in the EMO has led to enhanced performance on multiobjective optimization problems with complicated Pareto fronts (MOPs-cPF), but the high computational cost resulting from model training should not be taken as a natural expense. To overcome this drawback, this paper proposes an incremental learning-inspired mating restriction strategy (ILMR) for solving MOP-cPF efficiently. In ILMR, a mating restriction is implemented based on an incremental learning model that establishes the mating pool for each solution in the population and incrementally updates as the population evolves. Specifically, it consists of two interdependent parts, i.e., a learning module and a forgetting module. In one evolutionary cycle, the learning module is used to learn new knowledge from the high-quality offspring solutions, while the forgetting module is utilized to remove the information provided by relatively poor solutions in the population. Moreover, a multiobjective evolutionary algorithm with ILMR, named MEILM, is proposed and compared with six state-of-the-art algorithms on a variety of MOP-cPF. The experimental results show that there are significant improvements benefitting from the proposed mating restriction strategy.

A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection

Adaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the performance of evolutionary algorithms (EAs) by producing high-quality offspring during the evolutionary process. This paper proposes a novel classification tree based adaptive operator selection strategy for multi-objective evolutionary algorithm based on decomposition (MOEA/D-CTAOS). In our proposal, the classification tree is trained by the recorded data set which contains the information on the historical offspring. Before the reproduction at each generation, the classifier is used to predict each possible result obtained by different operators, and only one operator with the best result is selected to generate offspring next. Meanwhile, a novel differential evolution based on search inertia (SiDE) is designed to steer the evolutionary process in a more efficient way. The experimental results demonstrate that proposed MOEA/D-CTAOS outperforms other MOEA/D variants on UF and LZ benchmarks in terms of IGD and HV value. Further investigation also confirms the advantage of direction-guided search strategy in SiDE.

An effective hybrid search algorithm for the multiple traveling repairman problem with profits

The multiple traveling repairman problem with profits consists of multiple repairmen serving a subset of all customers to maximize the revenues collected through the visited customers. To address this problem, an effective hybrid search algorithm based on the memetic framework is proposed. In the proposed method, three features are integrated: a dedicated arc-based crossover to generate high-quality offspring solutions, a fast evaluation technique to reduce the complexity of navigating classical neighborhoods as well as a correcting step to ensure accurate evaluation of neighboring solutions. The performance of the algorithm on 470 benchmark instances were compared with those of the leading reference algorithms. The results show that the proposed algorithm outperforms the state-of-the-art algorithms by setting new records for 137 instances and matching the best-known results for 330 instances. The importance of the key search components of the algorithm was investigated.