Intelligent Water Drops Algorithm Research Articles

Optimal path planning for mobile robot using Intelligent Water Drops algorithm

In this paper, the generalized intelligent water drops (IWD) algorithm is proposed to solve robot path planning problem. Path planning is modeled by IWD algorithm, because there is inherent similarity between this and finding most appropriate route in natural rivers, that occurs by interaction between water drops and the river bed. The proposed algorithm has two levels; first level, finds the best global feasible path. Second level, performs local search at relatively near distances of global path and reduces its length and time to reach optimal solution. The IWD algorithm, like other inspired nature algorithms, has not a mechanism to deal with constrained optimization problem in its original version. So, a mechanism has been proposed based on repair of infeasible solutions. In this mechanism, a fuzzy inference system is proposed for selecting the best possible infeasible solution and then the local search operator repairs any of them in violation locations. Another innovation of this paper provides a mechanism to local soil update, which is based on fuzzy systems and is independent of size and complexity of environment and its soil altering will be defined in fixed range. Simulation result shows ability of IWD algorithm in finding of the optimal path.

Application of Intelligent Water Drops Algorithm in Reservoir Operation

Optimum reservoir operation is a challenging problem in water resources systems. In this paper, Intelligent Water Drops (IWD) algorithm is applied in a reservoir operation problem. IWD is a population based algorithm and is initially proposed for solving combinatorial problems. The algorithm mimics the dynamics of river system and the behavior of water drops in the rivers. For this purpose data from Dez reservoir, located in southwestern Iran, has been used to examine the performance of the model. Moreover, due to similarities between IWD and the Ant Colony Optimization (ACO) algorithms, the results are compared with those of the ACO algorithm. Comparison of the results shows that while the IWD algorithm finds relatively better solutions, it is able to overcome the computational time consumption deficiencies inherited in the ACO methods. This is very important in large models with too many decision variables where run time becomes a limiting factor for optimization model applications.

An improved intelligent water drops algorithm for solving multi-objective job shop scheduling

Multi-objective job shop scheduling (MOJSS) problems can be found in various application areas. The efficient solution of MOJSS problems has received continuous attention. In this research, a new meta-heuristic algorithm, namely the Intelligent Water Drops (IWD) algorithm is customized for solving the MOJSS problem. The optimization objective of MOJSS in this research is to find the best compromising solutions (Pareto non-dominance set) considering multiple criteria, namely makespan, tardiness and mean flow time of the schedules. MOJSS-IWD, which is a modified version of the original IWD algorithm, is proposed to solve the MOJSS problem. A scoring function which gives each schedule a score based on its multiple criteria values is embedded into the MOJSS-IWD’s local search process. Experimental evaluation shows that the customized IWD algorithm can identify the Pareto non-dominance schedules efficiently.

Improving K-means clustering algorithm with the intelligent water drops (IWD) algorithm

In this paper, the K-means algorithm for data clustering is improved by the swarm-based nature-inspired optimisation algorithm, the intelligent water drops (IWD) algorithm. The K-means algorithm is an iterative algorithm in which the number of clusters is given in advance. Although the K-means is fast to converge, it is sensitive to the initial conditions. As a result, it is often trapped in local optimums. The IWD algorithm, which mimics the actions and reactions between natural water drops in real rivers, is modified to implicitly embed in itself the main processes of the K-means algorithm. The modified algorithm called IWD-KM is tested with several well-known datasets for clustering, and its performance is compared with the K-means algorithm. The experimental results show the superiority of the proposed IWD-KM algorithm.

Optimisation of Manufacturing Process Models via Intelligent Water Drop Algorithm

In this paper, an intelligent water drop algorithm or IWD has been developed to optimise machining parameters in turning operation including a spring force model. Firstly, machining conditions are to minimise the production cost in conventional manufacturing processes. Several passes of rough machining are started on the turning operation with a final pass of a finishing. Various constraints are considered in each non-linear and non-convex model. The machining parameters in the turning consist of the depth of cut, cutting speed and feed. Finally, in a specialised manufacturing application on the spring force problem, an achievement of a specific goal may be the primary objective subject to some process parameter ranges. The computational results clearly showed that the proposed sequential procedures of the IWD have considerably improved the objective functions.

An Intelligent Water Drop Algorithm for Solving Optimal Reactive Power Dispatch Problem

Abstarct: This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. In this paper an intelligent water drop (IWD) algorithm has been proposed to solve this combinatorial optimization problem. Intelligent water drop algorithm is a swarm-based nature inspired optimization algorithm, which has been inspired from natural rivers. A natural river often finds good paths among lots of possible paths in its ways from source to destination and finally find almost optimal path to their destination. These ideas are embedded into proposed algorithm for solving reactive dispatch problem.

An improved Intelligent Water Drops algorithm for achieving optimal job-shop scheduling solutions

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.

An approach to continuous optimization by the Intelligent Water Drops algorithm

In this paper, the Intelligent Water Drops (IWD) algorithm is augmented with a mutation-based local search to find the optimal values of numerical functions. The proposed algorithm called the IWD-CO (IWD for continuous optimization) is tested with six different benchmark functions. The experimental results are satisfactory, which encourage further researches in this regard.

Using intelligent water drops algorithm for optimisation routing protocol in mobile ad-hoc networks

Mobile Ad–hoc networks (MANET), which operates with no pre–setup infrastructure, is one of the most active research fields. A thorough study on the basic architecture reveals that mobile nodes are relatively independent, self–managed and highly dynamic in MANET. Traditional routing protocols cannot therefore work properly in such environment. Therefore, this paper proposes a new optimisation routing protocol based on an Intelligent Water Drop (IWD) algorithm for MANET, which transmits simple packets containing basic IWD properties among neighbouring nodes without knowing the topology and the link condition of the whole network. This algorithm features a small traffic overhead, adaptation to the dynamic topology and support for redundant routing. The main feature of this new algorithm is that it calculates all possible of RNS (Relay Node Set) and then selects the set with minimum number of nodes by IWD algorithm. The algorithm demonstrates a good performance when it is compared with other routing protocols.

Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm

Energy conservation is an important aspect in wireless sensor networks (WSNs) to extend the network lifetime. In order to obtain energy-efficient data transmission within the network, sensor nodes can be organised into an optimal data aggregation tree with optimally selected aggregation nodes to transfer data. Various nature-inspired optimisation methods have been shown to outperform conventional methods when solving this problem in a distributed manner, that is, each sensor node makes its own decision on routing the data. In this study, a novel optimisation algorithm called intelligent water drops (IWDs) is adopted to construct the optimal data aggregation trees for the WSNs. Further enhancement of the basic IWD algorithm is proposed to improve the construction of the tree by attempting to increase the probability of selecting optimum aggregation nodes. The computational experiment results show that the IWD algorithm is able to obtain a better data aggregation tree with a smaller number of edges representing direct communication between two nodes when compared with the well-known optimisation method such as ant colony optimisation. In addition, the proposed improved version of the IWD algorithm provides better performance in comparison with the basic IWD algorithm for saving the energy of WSNs.

Intelligent water drops algorithm for automatic multilevel thresholding of grey-level images using a modified Otsu's criterion

In this paper, the newly introduced swarm-based optimisation algorithm called intelligent water drops (IWDs) algorithm is adjusted to optimise a modified Otsu’s criterion for automatic multilevel thresholding. The proposed algorithm simply called IWD-AMLT is tested with several grey-level images and its performance is assessed by the peak signal to noise ratio (PSNR) measure. The thresholded images obtained by the IWD-AMLT have high qualities according to the subjective judgement and the PSNR values.

An Intelligent Water Drop Algorithm for Solving Economic Load Dispatch Problem

Economic Load Dispatch (ELD) is a method of determining the most efficient, low-cost and reliable operation of a power system by dispatching available electricity generation resources to supply load on the system. The primary objective of economic dispatch is to minimize total cost of generation while honoring operational constraints of available generation resources. In this paper an intelligent water drop (IWD) algorithm has been proposed to solve ELD problem with an objective of minimizing the total cost of generation. Intelligent water drop algorithm is a swarm-based natureinspired optimization algorithm, which has been inspired from natural rivers. A natural river often finds good paths among lots of possible paths in its ways from source to destination and finally find almost optimal path to their destination. These ideas are embedded into the proposed algorithm for solving economic load dispatch problem. The main advantage of the proposed technique is easy is implement and capable of finding feasible near global optimal solution with less computational effort. In order to illustrate the effectiveness of the proposed method, it has been tested on 6-unit and 20-unit test systems with incremental fuel cost functions taking into account the valve point-point loading effects. Numerical results shows that the proposed method has good convergence property and better in quality of solution than other algorithms reported in recent literature. Keywords—Economic load dispatch, Transmission loss, Optimization, Valve point loading, Intelligent Water Drop Algorithm.

Improved intelligent water drops algorithm using adaptive schema

Intelligent water drops (IWD) algorithm is a new meta-heuristic approach belonging to a class of swarm intelligence-based algorithms. It is inspired from observing processes of natural water swarm that happen in the natural river systems. This paper presents an improved IWD algorithm based on developing an adaptive schema to prevent the IWD algorithm from premature convergence. The performance of the adaptive IWD is compared with original IWD and other meta-heuristic algorithms in solving travelling salesman problem (TSP). The results clearly show that the proposed algorithm has better performance than those of original IWD, and MIWD-TSP algorithm and very competitive results to others meta-heuristics.

Mitigating DDoS attacks with transparent and intelligent fast-flux swarm network

Distributed denial of service attacks are a great threat to service availability in cloud computing. In recent years, DDoS attacks have increased tremendously in bandwidth and technique. In this article, we propose a novel approach to mitigate DDoS attacks using an intelligent fast-flux swarm network. An intelligent swarm network is required to ensure autonomous coordination and allocation of swarm nodes to perform its relaying operations. We adapted the Intelligent Water Drop algorithm for distributed and parallel optimization. The fast-flux technique was used to maintain connectivity between swarm nodes, clients, and servers. Fast-flux service networks also allow us to build a transparent service, which allows minimal modifications of existing cloud services (e.g. HTTP, SMTP). A software simulation consisting of 400,000 client nodes and 10,000 swarm nodes has shown that we can maintain 99.96 percent packet delivery ratio when the network is under attack from a similarly sized DDoS network of 10,000 dedicated malicious nodes.

Novel intelligent water drops optimization approach to single UCAV smooth trajectory planning

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.

The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm

A natural river often finds good paths among lots of possible paths in its ways from the source to destination. These near optimal or optimal paths are obtained by the actions and reactions that occur among the water drops and the water drops with the riverbeds. The intelligent water drops (IWD) algorithm is a new swarm-based optimisation algorithm inspired from observing natural water drops that flow in rivers. In this paper, the IWD algorithm is tested to find solutions of the n-queen puzzle with a simple local heuristic. The travelling salesman problem (TSP) is also solved with a modified IWD algorithm. Moreover, the IWD algorithm is tested with some more multiple knapsack problems (MKP) in which near-optimal or optimal solutions are obtained.

Intelligent water drops algorithm

PurposeThe purpose of this paper is to test the capability of a new population‐based optimization algorithm for solving an NP‐hard problem, called “Multiple Knapsack Problem”, or MKP.Design/methodology/approachHere, the intelligent water drops (IWD) algorithm, which is a population‐based optimization algorithm, is modified to include a suitable local heuristic for the MKP. Then, the proposed algorithm is used to solve the MKP.FindingsThe proposed IWD algorithm for the MKP is tested by standard problems and the results demonstrate that the proposed IWD‐MKP algorithm is trustable and promising in finding the optimal or near‐optimal solutions. It is proved that the IWD algorithm has the property of the convergence in value.Originality/valueThis paper introduces the new optimization algorithm, IWD, to be used for the first time for the MKP and shows that the IWD is applicable for this NP‐hard problem. This research paves the way to modify the IWD for other optimization problems. Moreover, it opens the way to get possibly better results by modifying the proposed IWD‐MKP algorithm.