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

2016 ◽  
Vol 15 (03) ◽  
pp. 1650014 ◽  
Author(s):  
Maysam Orouskhani ◽  
Mohammad Teshnehlab ◽  
Mohammad Ali Nekoui
Keyword(s):  
Population Based ◽  
Selection Method ◽  
Ranking Method ◽  
Borda Count ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Cat Swarm Optimization ◽  
Before And After ◽  
Traditional Approaches

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.

scholarly journals Multi-Objective Optimization Design of an Electrohydrostatic Actuator Based on a Particle Swarm Optimization Algorithm and an Analytic Hierarchy Process

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2426 ◽  
Author(s):  
Bo Yu ◽  
Shuai Wu ◽  
Zongxia Jiao ◽  
Yaoxing Shang
Keyword(s):  
Particle Swarm Optimization ◽  
Power Consumption ◽  
Analytic Hierarchy Process ◽  
Optimization Design ◽  
Pareto Front ◽  
Multi Objective Optimization ◽  
Analytic Hierarchy ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Hierarchy Process

During the last few years, the concept of more-electric aircraft has been pushed ahead by industry and academics. For a more-electric actuation system, the electrohydrostatic actuator (EHA) has shown its potential for better reliability, low maintenance cost and reducing aircraft weight. Designing an EHA for aviation applications is a hard task, which should balance several inconsistent objectives simultaneously, such as weight, stiffness and power consumption. This work presents a method to obtain the optimal EHA, which combines multi-objective optimization with a synthetic decision method, that is, a multi-objective optimization design method, that can combine designers’ preferences and experiences. The evaluation model of an EHA in terms of weight, stiffness and power consumption is studied in the first section. Then, a multi-objective particle swarm optimization (MOPSO) algorithm is introduced to obtain the Pareto front, and an analytic hierarchy process (AHP) is applied to help find the optimal design in the Pareto front. A demo of an EHA design illustrates the feasibility of the proposed method.

scholarly journals A Distributed Multifactorial Particle Swarm Optimization Approach

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Beta Function ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New System

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>

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