Partheno-genetic Algorithm Research Articles

Global path planning and waypoint following for heterogeneous unmanned surface vehicles assisting inland water monitoring

The idea of dispatching multiple unmanned surface vehicles (USVs) to undertake marine missions has ignited a burgeoning enthusiasm on a global scale. Embarking on a quest to facilitate inland water monitoring, this paper presents a systematical approach concerning global path planning and path following for heterogeneous USVs. Specifically, by capturing the heterogeneous nature, an extended multiple travelling salesman problem (EMTSP) model, which seamlessly bridges the gap between various disparate constraints and optimization objectives, is formulated for the first time. Then, a novel Greedy Partheno Genetic Algorithm (GPGA) is devised to consistently address the problem from two aspects: (1) Incorporating the greedy randomized initialization and local exploration strategy, GPGA merits strong global and local searching ability, providing high-quality solutions for EMTSP. (2) A novel mutation strategy which not only inherits all advantages of PGA but also maintains the best individual in the offspring is devised, contributing to the local escaping efficiently. Finally, to track the waypoint permutations generated by GPGA, control input is generated by the nonlinear model predictive controller (NMPC), ensuring the USV corresponds with the reference path and smoothen the motion under constrained dynamics. Simulations and comparisons in various scenarios demonstrated the effectiveness and superiority of the proposed scheme.

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

BackgroundTremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data.ResultsIn this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein–Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with “inclusion” relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the “inclusion” relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases.ConclusionsThe gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.

A Novel Metaheuristic Hybrid Parthenogenetic Algorithm for Job Shop Scheduling Problems: Applying an Optimization Model

A Novel Metaheuristic Hybrid Parthenogenetic Algorithm for Job Shop Scheduling Problems: Applying an Optimization Model

Collaborative slot secondary allocation based on flight wave operation

PurposeFlights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.Design/methodology/approachTaking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.FindingsComparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.Originality/valueBased on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

Hybrid partheno-genetic algorithm for multi-depot perishable food delivery problem with mixed time windows

Hybrid partheno-genetic algorithm for multi-depot perishable food delivery problem with mixed time windows

Collection System Topology for Deep-Sea Offshore Wind Farms Considering Wind Characteristics

DC series-parallel collection system has recently been widely discussed for integrating deep-sea OWFs because it saves the offshore HVDC stations. However, comparing with traditional offshore electrical collection systems, there is a unique operational problem introducing by the DC series-parallel system that is the wind power curtailment caused by the wind power fluctuations. In this paper, a rigorous mathematical analysis of wind power curtailment is conducted, and an optimization model considering wind power curtailment for DC series-parallel collection systems is proposed. To address the particularities of the series-parallel collection system in graph theory, an improved parthenogenetic algorithm (PGA) is suggested to address the optimization problem. The proposed model and optimization algorithms are tested based on a deep-sea OWF with 56 wind turbines (WTs). The simulation results show that considering wind power curtailment during the DC series-parallel system planning helps to save the levelized cable cost of energy (LCCOE) in the long term, and putting the wiring direction of the series as perpendicular as possible to the prevailing wind direction of the OWF helps to increase the power generation of the OWF and reduce LCCOE.

IDM-SPS: Identifying driver module with somatic mutation, PPI network and subcellular localization

Mutation profiles together with prior knowledge such as interactions between genes/proteins provide abundant critical information for the identification of driver modules, which is very important for analyzing mutational heterogeneity in human cancers. Due to the negative effects of inevitable false positive interactions in the PPI network, subcellular localization data are exerted to filter out them firstly, and somatic mutation profiles are used to weight the retained interactions. Five novel recombination operators are introduced basing on the vertex degrees and the edge weights in the PPI network, and a parthenogenetic algorithm is devised for solving the presented identification model which takes into account network connectivity, mutual exclusivity, coverage, and hops between genes within a module. Extensive experimental results indicate that compared with two state-of-the-art computational methods Hotnet2 and MEXCOwalk, the proposed method exhibits competitive performance in most cases in terms of recovering known cancer genes, providing modules that have satisfied coverage and mutual exclusivity, and are enriched for mutations in specific cancer types. Many identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in the literature. In addition, the proposed method does identify many cancer related genes missed by methods Hotnet2 and MEXCOwalk, including some recognized genes covering many types of cancers but having low mutation frequency.

Optimizing Painting Sequence Scheduling Based on Adaptive Partheno-Genetic Algorithm

In this paper, we formulate and solve a novel real-life large-scale automotive parts paint shop scheduling problem, which contains color arrangement restrictions, part arrangement restrictions, bracket restrictions, and multi-objectives. Based on these restrictions, we construct exact constraints and two objective functions to form a large-scale multi-objective mixed-integer linear programming problem. To reduce this scheduling problem’s complexity, we converted the multi-objective model into a multi-level objective programming problem by combining the rule-based scheduling algorithm and the adaptive Partheno-Genetic algorithm. The rule-based scheduling algorithm is adopted to optimize color changes horizontally and bracket replacements vertically. The adaptive Partheno-Genetic algorithm is designed to optimize production based on the rule-based scheduling algorithm. Finally, we apply the model to the actual optimization problem that contained 829,684 variables and 137,319 constraints, and solved this problem by Python. The proposed method solves the optimal solution, consuming 575 s.

An evolvable hardware method based on elite Partheno-Genetic Algorithm

Aiming at the shortcomings of traditional genetic algorithm (GA) in the overall evolution of hardware, such as slow speed and small scale, a method of evolvable hardware (EHW) based on elite Partheno-Genetic Algorithm (PGA) is proposed. To improve the evolution efficiency of EHW, the number of chromosomes is reduced, the length of the chromosome is increased, the elite node chain is reserved, and the connection relationship of non-elite nodes is adjusted. The experimental results show that: compared with the existing literature, the evolution speed is increased 144 times on average, the overall scale of evolution is increased 74.7 times, and the evolution efficiency is significantly improved.

Research on Design and Dispatching of Circular Shuttle System

The shuttle is also called Rail Guide Vehicle (RGV). It has the characteristics of fast conveying speed, good flexibility, and high degree of automation. The circular RGV system is widely used in logistics network dispatching. The genetic operator of the ordinary partheno genetic algorithm is improved by the single-point transposition operator and the roulette method, and the improved partheno genetic algorithm is obtained. The improved partheno genetic algorithm is used to model and design the circular shuttle system.

An Improved Partheno-Genetic Algorithm for Open Path Multi-Depot Multiple Traveling Salesmen Problem

The Multiple traveling salesmen problem (MTSP) is a complex combinatorial optimization problem, which is an extension of the well-known traveling salesmen problem (TSP). Compared to TSP, MTSP is more suitable to model real-life problems. In this paper, an open path multi-depot multiple traveling salesmen problem (OPMDMTSP) is studied. For the problem studied, two different objectives are considered: minimizing the total cost of all sales staff and minimizing the longest travel length. For the OPMDMTSP, an improved partheno-genetic algorithm (IPGA) is proposed in this paper. In IPGA, a new selection operator that combining roulette selection and elitist selection is implemented. In addition, a more comprehensive mutation operation that introduces the propagation mechanism of invasive weed optimization algorithm is used. Extensive experiment that compares the proposed method with some state of the art methods shows that the IPGA is outperform other methods in terms of both solution quality and convergence ability.

Time-Dependent Theme Park Routing Problem by Partheno-Genetic Algorithm

With the improvement of people’s living standards and entertainment interests, theme parks have become one of the most popular holiday places. Many theme park websites provide a variety of information, according to which tourists can arrange their own schedules. However, most theme park websites usually have too much information, which makes it difficult for tourists to develop a tourism planning. Therefore, the theme park routing problem has attracted the attention of scholars. Based on the Traveling Salesman Problem (TSP) network, we propose a Time-Dependent Theme Park Routing Problem (TDTPRP), in which walking time is time-dependent, considering the degree of congestion and fatigue. The main goal is to maximize the number of attractions visited and satisfaction and to reduce queues and walking time. To verify the feasibility and the effectiveness of the model, we use the Partheno-Genetic Algorithm (PGA) and an improved Annealing Partheno-Genetic Algorithm (APGA) to solve the model in this paper. Then, in the experimental stage, we conducted two experiments, and the experimental data were divided into real-world problem instances and randomly generated problem instances. The results demonstrate that the parthenogenetic simulated annealing algorithm has better optimization ability than the general parthenogenetic algorithm when the data scale is expanded.

Two novel models and a parthenogenetic algorithm for detecting common driver pathways from pan-cancer data

With the rapid development of high-throughput sequencing technologies, huge volumes of generated cancer genomics data make it into reality to understand the carcinogenic pathogenesis from the molecular level. It is believed that the study of commonalities among different cancers is one of the significant problems for understanding cancers, and will be beneficial for personalized therapy and precision medicine in cancer treatment. The ComMDP method is a useful one for solving this problem. However, when there is a substantially difference among the number of samples, the method of accumulating the absolute weight value of every cancer, employed by the ComMDP method, may give rise to missing some driver pathways. In this paper, two mathematical models CDP-V and CDP-H, replacing the absolute weight values with relative ones, are presented by using variance and harmonic mean, respectively. By devising a sort of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm is proposed for solving these two models. Extensive experiments were performed on both simulated and real cancer data. The experimental results show that given several types of cancer, the gene sets identified based on the presented models and algorithm not only mutate in a large proportion of samples of these cancers, but have close proportion of mutated samples in each cancer. In addition, some biologically meaningful gene sets, which are missed by the ComMDP one, are indeed detected. Hence the identified methods based on the presented models and algorithm may become useful complementary tools for identifying cancer pathways.

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

Practical algorithms for allocating the roadside units in VANET

The vehicle-to-infrastructure communication is a kind of communication mode of the vehicular ad-hoc network (VANET). It is paramount to allocate roadside units (RSUs) appropriately to assure the quality of communication service. Sarubbi et al. have proposed a greedy heuristic allocation algorithm Delta-r. It examines the Δ ρ2 ρ1 -deployment constraint, a metric for evaluating the performance of VANET, with relative contact time. Unfortunately, the relative contact time of vehicles at urban cells may present `false high', which would play an opposite effect on making a decision. In order to solve the problem, a new allocation algorithm Delta-uc is presented, which examines the Δ ρ2 ρ1 -deployment constraint with a useful contribution. It gets rid of the negative effect of `extra' contact time by means of retaining merely the contributing relative duration of a vehicle at an urban cell. Furthermore, an effective recombination operator is introduced basing on algorithm Delta-uc, and a parthenogenetic algorithm UCPGA is presented to solve the allocation problem. Experimental comparisons were performed on the real mobility trace of Cologne, Germany. The experimental results demonstrate that in many Δ ρ2 ρ1 -deployments, the number of RSUs produced by the Delta-uc and UCPGA algorithms are, respectively, fewer than those produced by algorithms Delta-r and DeltaGA.

Joint Optimization of Computation Offloading and Task Scheduling in Vehicular Edge Computing Networks

Resource-intensive applications on smart vehicles is posing difficulties to the use of traditional cloud computing for computation offloading in vehicular networks. In particular, the long transmission distance between the vehicles and the cloud center can cause high latency and poor reliability which may degrade application performance and quality of service. As an integration of mobile edge computing and vehicular networks, vehicular edge computing is a promising paradigm that aims to improve vehicular services by performing computation offloading in close proximity to vehicles. In this paper, the task offloading algorithm that efficiently optimizes task delay and computing resource consumption in multi-user, multi-server vehicular edge computing scenarios is studied. The offloading algorithm not only determines where the tasks are performed, but also indicates the execution order of the tasks on the server. In order to reduce the time complexity, this paper proposes a hybrid intelligent optimization algorithm based on partheno genetic algorithm and heuristic rules. Extensive simulations are conducted, and the results show that compared with the baseline algorithms, the proposed algorithm effectively improves the offloading utility of the VEC system and is suitable for task offloading in various situations.

An Improved Partheno-Genetic Algorithm With Reproduction Mechanism for the Multiple Traveling Salesperson Problem

This paper considers the problem of the multiple traveling salesperson problem (MTSP) with multiple depots and closed path. We analyzed the advantages and disadvantages of Partheno-Genetic algorithm (PGA) in solving the MTSP. By integrating the reproduction mechanism in the invasive weed algorithm (IWO), we have solved the defect that the local information of individuals in the PGA population may be missing, and obtained the improved Partheno-Genetic algorithm with reproduction mechanism (RIPGA), which greatly improves the solution performance. By comparing the RIPGA with other GAs and other intelligent algorithms on a large number of traveling salesperson problem (TSP) test functions, we have well verified the solution performance of the RIPGA on the optimal solution, indicating that the algorithm can effectively avoid local convergence. At the same time, the algorithm is less affected by parameters and has good stability.

Ordinal optimisation approach for complex distribution network reconfiguration

The increasing complexity of the distribution system makes the practical distribution network operation much difficult. This paper presents an ordinal optimisation (OO) approach to solve the distribution network reconfiguration, which is an NP- hard problem with discrete control variables. OO uses crude and computationally fast model to reduce the search space. The spirit of OO is to seek the good enough solution instead of the best with high probability. The proposed approach is validated with two practical distribution systems, TPC 84 node test system. The results are compared with Optimal Flow Pattern (OFP), Common Genetic Algorithm (CGA), Partheno Genetic Algorithm with Tree Structure Encoding technology (TSE-PGA) and Second-Order Cone Programming (SOCP).

Identifying mutated driver pathways in cancer by integrating multi-omics data

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.

A state based energy optimization framework for dynamic virtual machine placement

The dynamic optimization of virtual machine (VM) placement is to dynamically adjust the placement of VMs on physical machines (PMs) to accomplish some objectives with certain constraints. On one hand, the number of possible combinations of PMs and VMs can be extremely large, which make the optimal solution very hard to get. On the other hand, the optimized solution needs be reachable from the old solution. To solve the problem from both sides, a partitioned optimization framework is proposed. First, four different states of PM, i.e. off, sleeping, ready and running, are introduced with different energy consumption. Running pool, sleeping pool and off pool are set up which partition PMs based on their different states. The classification helps us build the energy consumption model which is needed to evaluate mapping solutions. To decide if a new solution is better than the old one, only three parts of energy need be considered, i.e. energy changes for PMs in different states, energy consumed for changing the states of PMs, and extra energy consumption for migrating VMs. An energy model composed of these three parts is built as the optimization objective. A method is presented to decide the most suitable range to conduct the energy optimization through excluding some PMs in the sleep or off pool if the best solution achieved with those PMs included cannot be better than old solution. A memetic algorithm combining the partheno-genetic algorithm with the multiplayer random evolutionary game theory is proposed to achieve the global optimal solution and generate the executable live migration sequence from old mapping to the target one at the same time. According to our experimental results, our method can decrease the optimization range remarkably. Within the optimized scales, the proposed algorithm performed very well to approach the global optimal solution and guarantee the solution’s feasibility from old solution at the same time.