Multi-objective Cat Swarm Optimization Research Articles

A multi-objective cat swarm optimization algorithm based on two-archive mechanism for UAV 3-D path planning problem

Achieving a balance between convergence and diversity is crucial in addressing multi-objective optimization problems (MOPs). In this paper, a multi-objective cat swarm optimization based on a new two-archive mechanism (MOCSO_TA) is proposed for the above challenge. In this approach, solutions that can promote convergence are stored by the convergence archive (CA). While solutions that can enhance diversity in the population are saved in the diversity archive (DA). Path planning is a critical process for unmanned aerial vehicles (UAVs), involving the identification of a route that is short and secure. Multi-objective algorithms have become a crucial approach for addressing UAV path planning problem, thus motivating the use of the proposed MOCSO_TA in path planning problem. The proposed algorithm is compared with two sets of representative multi-objective algorithms on the DTLZ, WFG, and ZDT benchmark problems. The experimental results demonstrate the outstanding performance of MOCSO_TA. The effectiveness of the MOCSO_TA is demonstrated by designing two terrains and comparing it with various multi-objective algorithms. The results confirmed the superiority of MOCSO_TA.

Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms.

Conflicting Parameter Pair Optimization for Linear Aperiodic Antenna Array using Chebyshev Taper based Genetic Algorithm

In this study, the peak side lobe level (PSLL) in the radiation pattern of a linear antenna array (LAA) is lowered without affecting its first null beam width (FNBW). Antenna array synthesis is commonly applied to achieve high directivity, low side lobes, high gain and desired null positions in the output radiation pattern. But output parameters like PSLL, null positions and beam width conflict with each other, i.e. as one parameter improves, the other deteriorates. To avoid this problem, a multi-objective optimization algorithm can be implemented, in which both the conflicting parameters can be simultaneously optimized. This work proposes a multi-objective algorithm, which takes advantages of the well-known Chebyshev tapering and genetic algorithm (GA), to lower the PSLL without broadening the beam further. Array elements are fed using Chebyshev tapered excitations while GA is incorporated to optimize the elemental spacing. The results of 28-element LAA are compared with those of multi-objective Cauchy mutated cat swarm optimization (MO-CMCSO) existing in literature, which has also been proven to be superior to multi-objective cat swarm optimization (MO-CSO) and multi-objective particle swarm optimization (MO-PSO). Results indicate that the proposed algorithm performs better by further reducing the PSLL from -21.57 dB (MO-CMCSO) to -28.18 dB, while maintaining the same FNBW of 7.4 degrees.

Solution of The Combined Environmental Economic Dispatch Problem Using Multi-Objective Cat Swarm Optimization

Solution of The Combined Environmental Economic Dispatch Problem Using Multi-Objective Cat Swarm Optimization

Solution of The Combined Environmental Economic Dispatch Problem Using Multi-Objective Cat Swarm Optimization

Solution of The Combined Environmental Economic Dispatch Problem Using Multi-Objective Cat Swarm Optimization

An Effective Bi-Stage Method for Renewable Energy Sources Integration into Unbalanced Distribution Systems Considering Uncertainty

The output generations of renewable energy sources (RES) depend basically on climatic conditions, which are the main reason for their uncertain nature. As a result, the performance and security of distribution systems can be significantly worsened with high RES penetration. To address these issues, an analytical study was carried out by considering different penetration strategies for RES in the radial distribution system. Moreover, a bi-stage procedure was proposed for optimal planning of RES penetration. The first stage was concerned with calculating the optimal RES locations and sites. This stage aimed to minimize the voltage variations in the distribution system. In turn, the second stage was concerned with obtaining the optimal setting of the voltage control devices to improve the voltage profile. The multi-objective cat swarm optimization (MO-CSO) algorithm was proposed to solve the bi-stages optimization problems for enhancing the distribution system performance. Furthermore, the impact of the RES penetration level and their uncertainty on a distribution system voltage were studied. The proposed method was tested on the IEEE 34-bus unbalanced distribution test system, which was analyzed using backward/forward sweep power flow for unbalanced radial distribution systems. The proposed method provided satisfactory results for increasing the penetration level of RES in unbalanced distribution networks.

Multi-Objective Modified Grey Wolf Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network

With the advancement toward 6G technology, mobile data growth is estimated to increase many fold. There will also be an increase in the control plane load (IoT, IoE). Such problems call for the technologies that can efficiently utilize the resources to optimize the system performance, and the possible solution is cognitive radio technology. As the spectrum sensing is the key enabler of cognitive radio technology, in this paper, the multi-objective parameters defining the efficiency of spectrum sensing for a cognitive radio network (CRN), which are throughput, interference, and energy efficiency, defined in terms of sensing time, power allocation, and detection threshold are dealt. In this paper, a novel Multi-Objective Modified Grey Wolf Optimization (MOMGWO) algorithm is proposed to solve the multi-objective optimization problem in the field of spectrum sensing in a cognitive radio network which is an important paradigm in wireless communication technology. Modification in Grey Wolf Optimization (GWO) is applied to balance the trade-off between exploration and exploitation process in conventional GWO, to obtain global optima. Modification is introduced in terms of mutation in leader selection, discrimination weight, and mutation coefficient. The non-dominated solution set of the proposed algorithm is compared with the existing algorithms like Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Cat Swarm Optimization (MOCSO), and conventional Multi-Objective Grey Wolf Optimization (MOGWO) algorithm. The simulation result shows that the proposed MOMGWO has outperformed the existing algorithms with respect to the quality of the Pareto front. Thus, the best solutions for the spectrum sensing parameters in optimizing multi-objective problems for cognitive radio network can be obtained via the proposed MOMGWO algorithm.

A Path Planning Method Based on Multi- Objective Cauchy Mutation Cat Swarm Optimization Algorithm for Navigation System of Intelligent Patrol Car

The intelligent patrol car with environmental sensing and autonomous navigation is a special robot, which is mostly used for equipment defect detection in industrial areas such as the power distribution room or data center room. A path planning algorithm for the navigation system of intelligent patrol car is proposed to ensure efficient and secure navigation in the complex indoor environment, and its effect is verified by simulation and experiment. First, a patrol car platform integrated with several intelligent devices is built to achieve global localization, mapping and path planning. Then a new co-optimization on multi-objective Cauchy mutation cat swarm optimization (MOCMCSO) and artificial potential field method (APFM) is proposed to solve the multi-objective optimization problem on shortest global path length and minimum total turning-angle variation. The optimal path is written into the navigation module to drive the patrol car to move and navigate. The simulations are carried out to confirm that the method can achieve a balance between the shortest path and good path smoothness, which has less optimization time and lower fitness value compared with multi-objective cat swarm optimization (MOCSO) and multi-objective particle swarm optimization (MOPSO), and is more suitable for global path planning in indoor environment. Finally, the experiments have been carried out in the data center equipment room to verify the effectiveness and superiority over the path planning algorithm on MOCMCSO.

Evolutionary dynamic multi-objective optimization algorithm based on Borda count method

In this paper, a novel dynamic multi-objective optimization algorithm is introduced. The proposed method is composed of three parts: change detection, response to change, and optimization process. The first step is to use Sentry solutions to detect the environmental change and advises the algorithm when a change occurs. Then, to increase the diversity of solutions, the worst solutions should be elected and removed from population and re-initialized with new solutions. The main idea is to use Borda count method which is an optimal rank aggregation technique that ranks the solutions in order of preference and nominates the worst solutions that should be removed. The last step is optimization process which is done by multi-objective Cat swarm optimization (CSO) in this paper. CSO utilizes the population that has been improved from the previous step to estimate the best solutions and converges to optimal Pareto front. The performance of the proposed algorithm is tested on dynamic multi-objective benchmarks, and the results are compared with the ones achieved by previous algorithms. The simulation results indicate that the proposed algorithm can effectively track the time-varying optimal Pareto front and achieves competitive results in comparison with traditional approaches.

Integration of Cat Swarm Optimization and Borda Ranking Method for Solving Dynamic Multi-Objective Problems

This paper introduces a dynamic multi-objective optimization algorithm integrated of Cat swarm algorithm and Borda count ranking method. In the proposed approach, the cats of population are ranked and sorted based on Borda selection method just before and after a change. Then the cats with the worst Borda’s rank are re-initialized to improve the diversity of population. Also, a multi-objective cat swarm optimization (CSO) as a population-based evolutionary algorithm is applied to converge to optimal front. The simulation results indicate that the proposed algorithm achieves competitive results in comparison with traditional approaches.