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Job-shop scheduling is a typical NP-hard problem which has drawn continuous attention from researchers. In this paper, the Intelligent Water Drops (IWD) algorithm, which is a new meta-heuristics, is customised for solving job-shop scheduling problems. Five schemes are proposed to improve the original IWD algorithm, and the improved algorithm is named the Enhanced IWD algorithm (EIWD) algorithm. The optimisation objective is the makespan of the schedule. Experimental results show that the EIWD algorithm is able to find better solutions for the standard benchmark instances than the existing algorithms. This paper has made a contribution in two aspects. First, to the best of the authors’ knowledge, this research is the first to apply the IWD algorithm to the job-shop scheduling problem. This work can inspire further studies of applying IWD algorithm to other scheduling problems, such as open-shop scheduling and flow-shop scheduling. Second, this research further improves the original IWD algorithm by employing five schemes to increase the diversity of the solution space as well as the solution quality. [ABSTRACT FROM PUBLISHER]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Trajectory planning of unmanned combat aerial vehicle (UCAV) is a rather complicated global optimum problem in UCAV mission planning. Intelligent water drops (IWD) algorithm is newly presented under the inspiration of the dynamic of river systems and the actions that water drops do in the rivers, and it is easy to combine with other methods in optimization. In this paper, we propose an improved IWD optimization algorithm for solving the single UCAV smooth trajectory planning problems in various combating environments. The water drops can act as an agent in searching the optimal UCAV trajectory. The detailed realization procedure for this novel approach is also presented. In order to make the optimized UCAV trajectory more feasible for exact flying, an efficient path smoothing method called the κ-trajectory is adopted for smoothing the single UCAV trajectory. Series experimental comparison results show the proposed IWD optimization algorithm is more effective and feasible in the single UCAV smooth trajectory planning than the basic IWD model. [Copyright &y& Elsevier]