Discrete Invasive Weed Optimization Research Articles

An effective discrete invasive weed optimization algorithm for multi-AGVs dispatching problem with specific cases in matrix manufacturing workshop

This paper mainly studies the dynamic multiple automatic guided vehicles dispatching problem (AGVDP) with special cases in matrix manufacturing workshop. The purpose is to introduce dynamic strategy into the static dispatching process to improve the ability to deal with special situations in the scheduling process. To solve this problem, a mathematical model is formulated based on actual production. A new discrete invasive weed optimization algorithm (DIWO) with rescheduling strategy is proposed. A dynamic rescheduling strategy is proposed to improve the ability of scheduling system. A strategy of setting emergency waiting time is proposed based on dynamic rescheduling strategy. A heuristic is proposed to dynamically update the task list, which can effectively update the current task list, solving the problem of task update in special cases and the deletion of the task that has been dispatched. An insertion based local search is used to increase the local search ability. A comprehensive simulation experiment based on the 110 instances from an actual production factory validates the effectiveness of the proposed algorithm.

An improved PTS scheme based on a novel discrete invasive weed optimization algorithm for PAPR reduction in the UFMC signal

An improved PTS scheme based on a novel discrete invasive weed optimization algorithm for PAPR reduction in the UFMC signal

A hybrid weed optimized coverage path planning technique for autonomous harvesting in cashew orchards

A coverage path planning algorithm is proposed for discrete harvesting in cashew orchards. The main challenge in such an orchard is the collection of fruits and nuts lying on the floor. The manual collection of fruits and nuts is both time consuming and labour intensive. The scenario begs for automated collection of fruits and nuts. There are methods developed in research for continuous crop fields, but none for discrete coverage. The problem is visualized as a graph traversal problem and paths for autonomous maneuvering are generated. A novel Mahalanobis distance based partitioning approach for performing coverage is introduced. The proposed path planner was able to achieve a mean coverage of 52.78 percentage with a deviation of 18.95 percentage between the best and worst solutions. Optimization of the generated paths is achieved through a combination of local and global search techniques. This was implemented by combining a discrete invasive weed optimization technique with an improved 2-Opt operator. A case study is formulated for the fruit picking operations in the orchards of Puducherry. The performance of the proposed algorithm is benchmarked against existing methods and also with performance metrics such as convergence rate, convergence diversity and deviation ratio. The convergence rate was observed to be 99.97 percent and 97.83 percent for a dataset with 48 and 442 nodes respectively. The deviation ratio was 0.02 percent and 2.16 percent, with a convergence diversity of 1.18 percent and 30.14 percent for datasets with 48 and 442 nodes. The achieved solutions was on par with the global best solutions achieved so far for the test datasets.

Effective invasive weed optimization algorithms for distributed assembly permutation flowshop problem with total flowtime criterion

Abstract Distributed assembly permutation flowshop scheduling problem (DAPFSP) has important applications in modern assembly systems. In this paper, we present three variants of the discrete invasive weed optimization (DIWO) for the DAPFSP with total flowtime criterion. For solving such a problem, we present a two-level representation that consists of a product permutation and a number of job sequences. We introduce neighbourhood operators for both the product permutation and job sequences. We design effective local search procedures respectively for product-permutation-based neighbourhood and job-sequence-based neighbourhood. By combining the problem-specific knowledge and the idea of invasive weed optimization, we present three DIWO-based algorithms: a two-level discrete invasive weed optimization (TDIWO), a discrete invasive weed optimization with hybrid search operators (HDIWO), and a HDIWO with selection probability. The algorithms explore the two neighbourhoods in quite a different way. We calibrate the presented DIWO algorithms by means of the design of experimental method, and carry out a comprehensive computational campaign based on the 810 benchmark instances in the literature. The numerical experiments show that the presented DIWO algorithms perform significantly better than the other competing algorithms in the literature. Among the proposed algorithms, HDIWO is the best one.

An efficient discrete invasive weed optimization for blocking flow-shop scheduling problem

This paper proposes a discrete invasive weed optimization (DIWO) to solve the blocking flow-shop scheduling problem (BFSP) with makespan criterion, which has important practical applications in modern industry. In the proposed DIWO, an effective heuristic and the random method are combined to generate an initial plant population with high quality and diversity. To keep the searching ability and efficiency, a random-insertion-based spatial dispersal is presented by means of the normal distribution. Moreover, a shuffle-based referenced local search is embedded to further enhance local exploitation ability. An improved competitive exclusion is developed to determine an offspring plant population with good quality and diversity. The parameters setting is investigated based on a design-of-experiment approach. The effectiveness and applicability of the proposed spatial dispersal and local search are confirmed through numerical comparisons. Finally, a comprehensive computational evaluation including several state-of-the-art algorithms, together with statistical analyses, show that the proposed DIWO algorithm produces better results than all compared algorithms by significant margin.

A multi-objective discrete invasive weed optimization for multi-objective blocking flow-shop scheduling problem

The flow-shop scheduling problem with blocking constraints has received an increasing concern recently. However, multiple scheduling criteria are rarely considered simultaneously in most research. Therefore, in this paper, a multi-objective blocking flow-shop scheduling problem (MOBFSP) that minimizes the makespan and total tardiness simultaneously is investigated. To address this problem, a multi-objective discrete invasive weed optimization (MODIWO) algorithm is proposed. In the proposed MODIWO, a high-quality and diversified initial population is firstly constructed via two heuristics and varying weighed values. Then, a reference line-based reproduction and a sliding insertion-based spatial dispersal are developed to guide the global exploration and local exploitation of algorithm. Meanwhile, to enhance intensification search in local region, a self-adaption phase is introduced, which is implemented by a Pareto-based two stage local search with speedup mechanism. Furthermore, a new competitive exclusion strategy is also embedded to construct a superior population for the next generation. Finally, extensive computational experiments and comparisons with several recent state-of-the-art algorithms are carried out based on the well-known benchmark instances. Experimental results demonstrate the efficiency and effectiveness of the proposed MODIWO in solving the considered MOBFSP.

Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery

This article formulates an inventory routing problem in which backorders are allowed, each vehicle is used at most once, a central depot distributes a single product to a set of customers with an incremental delivery, each customer is served at most once over a finite planning period and the objective is minimizing transportation and inventory costs. Since the proposed model is an Np-Hard problem, for solving large scale problems two meta-heuristics, discrete invasive weed optimization and Genetic algorithm are presented. Tuning the parameters of the algorithms are performed by regression approach. In this approach, equation of fitness is found in terms of the parameters and the best value of the parameters is found in a way that the equation is minimized. Performance of the algorithms for solving the IRP is compared with statistical and multi-attribute decision making approach in terms of computational time and quality of solutions.

Self-adaptive discrete invasive weed optimization for the blocking flow-shop scheduling problem to minimize total tardiness

This paper proposes a self-adaptive discrete invasive weed optimization (SaDIWO) to solve the blocking flow-shop scheduling problem (BFSP) with the objective of minimizing total tardiness which has important applications in a variety of industrial systems. In the proposed SaDIWO, an improved NEH-based heuristic is firstly presented to generate an initial solution with high quality. Then, to guide the global exploration and local exploitation, a self-adaptive insertion-based spatial dispersal is presented. A distance-based competitive exclusion is developed to strike a compromise between the quality and diversity of offspring population. A variable neighborhood search with a speed-up mechanism is embedded to further enhance exploitation in the promising region around the individuals. Afterward, the parameters setting and the effectiveness of each component of the proposed algorithm are investigated through numerical experiments. The performance of the proposed algorithm is evaluated by comparisons with the existing state-of-the-art algorithms in the literature. Experimental results show that the proposed algorithm outperforms the existing state-of-the-art algorithms. Furthermore, the proposed SaDIWO also improves the best known solutions for 132 out of 480 problem instances.

A Discrete Hybrid Invasive Weed Optimization Algorithm for the Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem (CVRP) has been proved to be NP complete problem. The CVRP is not just a purely academic construct, it has many applications in practice. In this work, a discrete invasive weed optimization algorithm (DIWO) is proposed to solve the capacitated vehicle routing problem. Adaptive mutation and crossover in the genetic operation process are introduced to ensure the diversity of the algorithm and prevent it from falling into a local optimal solution with premature convergence. We use real matrix encoding and construct a discretization process for the subgeneration in the parent generation region. An improved 2-Opt and exchange operations structure based on the property of the problem is proposed to construct the two-stage hybrid variable-domain search method, strengthening the capacity of the local and global search ability of the algorithms. Comparing the experimental simulation and the algorithm with literature for different scale benchmarks proves that the DIWO algorithm is simple, efficient, adaptable, and robust for discrete combinatorial optimization problems.

An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems

Lot-streaming scheduling problem has been an active area of research due to its important applications in modern industries. This paper deals with the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion. An effective discrete invasive weed optimization (DIWO) algorithm is presented with new characteristics. A job permutation representation is utilized and an adapted Nawaz–Enscore–Ham heuristic is employed to ensure an initial weed colony with a certain level of quality. A new spatial dispersal model is designed based on the normal distribution and the property of tangent function to enhance global search. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. The presented DIWO is calibrated by means of the design of experiments approach. A comparative evaluation is carried out with several best performing algorithms based on a total of 280 randomly generated instances. The numerical experiments show that the presented DIWO algorithm produces significantly better results than the competing algorithms and it constitutes a new state-of-the-art solution for the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion.

Solving a multi-objective chance-constrained hub covering location problem by discrete invasive weed optimization

AbstractThis paper presents a stochastic bi-objective model for a single-allocation hub covering problem (HCP) with the variable capacity and uncertainty parameters. Locating hubs can influence the performance of hub and spoke networks, as a strategic decision. The presented model optimizes two objectives minimizing the total transportation cost and the maximum transportation time from an origin to a destination simultaneously. Then, due to the NP-hardness of the multi-objective chance-constrained HCP, the presented model is solved by a well-known meta-heuristic algorithm, namely multi-objective invasive weed optimization. Additionally, the associated results are compared with a well-known multi-objective evolutionary algorithm, namely non-dominated sorting genetic algorithm. Furthermore, the computational results of the foregoing algorithms are reported in terms four well-known metrics, namely quality, spacing, diversification, and mean ideal distance. Finally, the conclusion is reported.

A discrete invasive weed optimization algorithm for solving traveling salesman problem

The Traveling Salesman Problem (TSP) is one of the typical NP-hard problems. Efficient algorithms for the TSP have been the focus on academic circles at all times. This article proposes a discrete invasive weed optimization (DIWO) to solve TSP. Firstly, weeds individuals encode positive integer, on the basis that the normal distribution of the IWO does not change, and then calculate the fitness value of the weeds individuals. Secondly, the 3-Opt local search operator is used. Finally, an improved complete 2-Opt (I2Opt) is selected as a second local search operator for solve TSP. A benchmarks problem selected from TSPLIB is used to test the algorithm, and the results show that the DIWO algorithm proposed in this article can achieve to results closed to the theoretical optimal values within a reasonable period of time, and has strong robustness.

An Efficient Discrete Invasive Weed Optimization Algorithm for Web Services Selection

Efficient algorithm is required to select component services with end-to-end QoS constraints in dynamic composition of Web Services while optimizing the QoS of the composite service. In this paper, the services selection probelm is modeled as a nonlinear optimization with constraints, then a novel discrete invasive weed optimization web services selection algorithm is proposed. The proposed solution consists of two steps:first, a set of randomly generated feasible solutions are transformed into decimal code. Second, We utilize Gaussian diffusion to guide the population to spread in the solution space. The mutation probability and mutation step size of individual is dynamically adjusted by changing the stantard deviation of Gaussian distribution. Accordingly, the population diversity is ensured in the early stage to expand the search space, while the local search nearby excellent individuals is focused in the latter stage to ensure the global convergence. Theoretical analysis and experiment results indicate the efficiency, robustness and feasibility of our approach.