Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

Abstract

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.

Evaluation and Selection of the Quality Methods for Manufacturing Process Reliability Improvement—Intuitionistic Fuzzy Sets and Genetic Algorithm Approach

“…Many researchers suggest applying GA for solving KPs [24,25]. Initially, GA generates randomly a population consisting of representative individuals (chromosomes) over which genetic operators of mutation, crossbreeding, and selection are successively applied.…”

Collaborative multicenter logistics delivery network optimization with resource sharing

“…Heuristic algorithms are often used to solve the vehicle routing problem [ 37 , 38 ], parameter selection optimization [ 39 , 40 ], and multi-objective optimization problems [ 41 – 43 ]. For example, Dondo et al [ 44 ] developed a mixed-integer linear programming model to solve a large-scale MDVRPTW and then utilized an improved hybrid local search algorithm to obtain feasible routes.…”

Enhancement of Firefly Technique for Effective Optimization of Multi-Depot Vehicle Routing

“…and it is quicker to compute 1/ (1+dr 2 ) than an exponential function and if required it can be suitably substituted by . Distance among fireflies i and j at dx i , dx j is calculated by Euclidean distance [17].…”

“…6 (b) 1-1 interchange (inter-route operator) The routing process is considered when the route duration and vehicle capacity conditions are not violated while appending the customer to the present route and it is sustained till every customer has been allotted exactly to single route and when a route is initiated if a customer is inconsistent to the exceeding constraints that could not be inserted to current route due to satisfy the objective needs. The proposed technique was tested using Cordeau benchmark dataset of MDVRP problem and the outcomes were compared with ACO [16], PSO [4], GA [3], GVNS [15], GC [3], IWD [2], NGA [6], GAPR [3] algorithm. The results obtained by using enhanced firefly algorithm are compared with the metaheuristics and heuristic algorithms and by testing them on MDVRP instances ranging from class I to class II instances.…”