Discrete differential evolutions for the discounted {0-1} knapsack problem

An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem

This paper deals with the total weighted tardiness and earliness penalties for lot-streaming flow shop scheduling problems. A discrete differential evolution (DDE) algorithm with job permutations based representation is proposed. In the proposed DDE algorithm, the DE-based evolution is used to perform global exploitation, and a local search procedure based on the insert and swap neighborhood structure is used to enhance the exploration capability, and a restart scheme is employed to avoid the stagnation of the evolution. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DDE algorithm.

Cutting tool wear degrades the product quality in manufacturing processes. Hence, real-time online estimation of tool wear is important for suggesting a tool replacement before the wear limit is reached, in order to protect the workpiece and the CNC machine from damage and breakdown. In this study, using both statistical features and wavelet features extracted from sensor signals, an adaptive evolutionary extreme learning machine (ELM) learning paradigm is developed for tool wear estimation in high-speed milling process. In the proposed method, a discrete differential evolution (DE) algorithm is used to select input features for the ELM, and a continuous DE algorithm is used for parameter optimisation of the mixed kernel function for the ELM. The experimental results indicate that the proposed adaptive evolutionary ELM-based tool wear estimation model can effectively estimate the tool wear in high-speed milling process. Empirical comparisons show that the proposed model performs better than existing approaches in estimating the tool wear.

Aiming at the stochastic vehicle routing problems with simultaneous pickups and deliveries, a novel discrete differential evolution algorithm is proposed for routes optimization. The algorithm can directly be used for the discrete domain by special design. Computational simulations and comparisons based on two kinds of problems of different sizes of SVRPSPD are provided. Results demonstrate that the proposed algorithm obtains better results than the basic differential evolution algorithm and the existing genetic algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2346 Full Text: PDF

In a traveling salesman problem, if the set of nodes is divided into clusters so that a single node from each cluster can be visited, then the problem is known as the generalized traveling salesman problem where the objective is to find a tour with minimum cost passing through only a single node from each cluster. In this chapter, a discrete differential evolution algorithm is presented to solve the problem on a set of benchmark instances. The discrete differential evolution algorithm is hybridized with local search improvement heuristics to further improve the solution quality. Some speed-up methods presented by the authors previously are employed to accelerate the greedy node insertion into a tour. The performance of the hybrid discrete differential evolution algorithm is tested on a set of benchmark instances with symmetric distances ranging from 51 (11) to 1084 (217) nodes (clusters) from the literature. Computational results show its highly competitive performance in comparison to the best performing algorithms from the literature.

Unit commitment is a typical complex multi-constraint and non-linear optimization problem in power system dispatch. With the expansion of the scale of the power system, the thermal power unit combination has problems such as low accuracy and difficulty in solving. In response to these problems, this paper proposes an algorithm that combines discrete differential evolution and quadratic programming to solve hierarchically. First, the upper-level problem uses Discrete Differential Evolution (DDE) to solve the start-stop state of the unit. In the process of solving, replace the randomly generated initial value with the startup evaluation value, and repair the unit status based on the principle of “non-stop, only startup, and priority startup of large-capacity units”. In order to further optimize the performance of the discrete differential evolution algorithm and prevent particles from falling into the local area, an adaptive strategy is adopted to update the mutation factor and other parameters. In addition, a new mutation strategy is adopted in the discrete differential evolution to balance the early development ability and the later exploration ability. Then the lower-level problem uses Quadratic Programming (QP) to carry out economic dispatch for the units that meet the constraint conditions such as the start and stop time of the unit. Finally, a system of 10 to 100 computers is used for simulation analysis. The experimental results show that the algorithm has fast convergence speed, high solution accuracy, and good robustness.

An artificial immune differential evolution algorithm for scheduling a distributed heterogeneous flexible flowshop

A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems

Safety is one concern that hinders the acceptance of ridesharing in the general public. Several studies have been conducted on the trust issue in recent years to relieve this concern. The introduction of trust in ridesharing systems provides a pragmatic approach to solving this problem. In this study, we will develop a trust-aware ridesharing recommender system decision model to generate recommendations for drivers and passengers. The requirements of trust for both sides, drivers and passengers, are taken into consideration in the decision model proposed in this paper. The decision model considers the factors in typical ridesharing systems, including vehicle capacities, timing, location and trust requirements, etc. The decision model aims to determine the shared rides that minimize cost while respecting the trust and relevant constraints. As the decision problem is a nonlinear integer programming problem, we combine a self-adaptive neighborhood search with Differential Evolution to develop an algorithm to solve it. To assess the effectiveness of the proposed algorithm, several other evolutionary computation approaches are also applied to solve the same problem. The effectiveness assessment is done based on the performance of applying different algorithms to find solutions for test cases, to provide a guideline for selecting a proper solution approach.

The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP.

A hybrid discrete differential evolution algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion

With the expanding scope of human activities in marine environments, the efficient detection and tracking of mobile targets on the ocean's surface have become increasingly crucial. SAR constellation can obtain ground observation data based on user requests and subject to visibility conditions. Now it is an indispensable tool in sea surface moving target search Tasks. Satellite constellation resources are scarce and limited, and user demands are diverse. How to rationally dispatch satellite constellation resources to meet user needs to the maximum extent and improve the application efficiency of satellite resources is an urgent scientific problem that needs to be solved. This paper mainly expounds two respects of work: firstly, modeling SAR constellation scheduling problem for sea surface moving target search tasks to establish the objective function; secondly, a novel multi-strategy discrete constrained differential evolution algorithm denoted as MSDCDE is proposed in the paper. The proposed MSDCDE algorithm integrates cross strategy based on discrete variables, constraint handling techniques, population restart strategy, and left-shift local strategy, which can effectively avoid falling into local optimality, thereby achieving global optimality and improving search and rescue performance. Six sets of experiments, totaling 215 runs, have been conducted to validate the effectiveness of the proposed resolution process framework and the MSDCDE algorithm. The proposed method demonstrated an over <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">48.98%</b> performance improvement compared to some state-of-the-art algorithms and significantly reduced task completion time.

Modeling and solution for hybrid flow-shop scheduling problem by two-stage stochastic programming

This article studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointer-based discrete differential evolution (PDDE) algorithm was developed with a dynamic programming (DP) algorithm embedded to solve the one-crane subproblem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4 h) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use. Note to Practitioners-Bridge cranes are commonly used to move heavy items in manufacturing and logistics systems. Generally, more than one crane runs on a common track. The latest versions of such cranes, such as those used in slab yards in the steel industry, can hold two items simultaneously. Operations scheduling of multiple double-load cranes involves the assignment of tasks to the cranes, the combination of tasks to double-load operations, and sequencing of the tasks, considering the noncrossing constraint between cranes. Effective solution of this complex problem can help to fully utilize the crane capability, increase productivity, and reduce energy consumption. This article models this problem and develops a heuristic solution that combines differential evolution (DE) and DP. Experiment results show that the algorithm is effective and efficient for practical use in slab yards. It may also be applicable to other systems using similar cranes.

A discrete differential evolution with local search particle swarm optimization to direct angle and aperture optimization in IMRT treatment planning problem