Adaptive Cat Swarm Optimization Algorithm and Its Applications in Vehicle Routing Problems

Abstract

This paper proposes a novel hybrid algorithm named Adaptive Cat Swarm Optimization (ACSO). It combines the benefits of two swarm intelligence algorithms, CSO and APSO, and presents better search results. Firstly, some strategies are implemented to improve the performance of the proposed hybrid algorithm. The tracing radius of the cat group is limited, and the random number parameter r is adaptive adjusted. In addition, a scaling factor update method, called a memory factor y, is introduced into the proposed algorithm. They can be learnt very well so as to jump out of local optimums and speed up the global convergence. Secondly, by comparing the proposed algorithm with PSO, APSO, and CSO, 23 benchmark functions are verified by simulation experiments, which consists of unimodal, multimodal, and fixed-dimension multimodal. The results show the effectiveness and efficiency of the innovative hybrid algorithm. Lastly, the proposed ACSO is utilized to solve the Vehicle Routing Problem (VRP). Experimental findings also reveal the practicability of the ACSO through a comparison with certain existing methods.

Transportation Risk Control of Waste Disposal in the Healthcare System with Two-Echelon Waste Collection Network

“…Heuristic algorithms are usually used to solve VRP and obtain near-optimal solutions in a reasonable time. Global search algorithms like genetic algorithm and PSO are applicable for finding the global optimum [24,25]. For local search heuristics, some metaheuristics are applied to accelerate convergence while avoid trapping in local optimum, such as Squeaky Wheel Optimization [26,27], Critical-Shaking Neighbourhood Search [28,29], and column generationbased heuristic [30].…”

Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles

“…Various experiments were carried out on the halfphysical simulation platform to test the performance of the controller. For verifying the results, the adaptive cat swarm optimization (ACSO) in [55] and the classic centralized logistic controller are used to compare with the proposed distributed controller. As the optimization algorithm has a feature of randomness, we generate multiple maps randomly and repeat simulation of each map multiple times.…”

“…For each map, three kinds of controllers are tested. e first controller is a classic centralized PSO controller, the second one is the ACSO controller in [55], and the last one is our DLC controller. For each simulation, we use a robot swarm consisting of four UGVs to deliver packages.…”

Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm

“…Artificial Bee Colony (ABC) [19], [20] simulated the honey gathering process of bees, this algorithm has a fast convergence speed to find global optimal solution. Cat Swarm Optimization (CSO) [21], [22] depicted the cats' search and tracking strategy. Grey Wolf Optimization (GWO) [23], [24] is an optimal search method which is designed by the gray wolves' predation activities.…”

“…The terminal voltage step response of PID controller tuned by four algorithms for G1(s)(0 ≤ K P , K I , K D ≤ 300)In order to further examine the performance of the proposed algorithm, another system(22) [54] is examined for the PID tuning problem.…”