scholarly journals On the Integrity of Performance Comparison for Evolutionary Multi-objective Optimisation Algorithms

2018 ◽  
pp. 3-15 ◽  
Author(s):  
Kevin Wilson ◽  
Shahin Rostami
Keyword(s):  
Performance Comparison ◽  
Multi Objective ◽  
Optimisation Algorithms

scholarly journals Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems

2015 ◽  
Vol 87 ◽  
pp. 47-60 ◽  
Author(s):  
Alexandru-Ciprian Zăvoianu ◽  
Edwin Lughofer ◽  
Werner Koppelstätter ◽  
Günther Weidenholzer ◽  
Wolfgang Amrhein ◽  
...  
Keyword(s):  
Steady State ◽  
Evolutionary Algorithms ◽  
Performance Comparison ◽  
Multi Objective ◽  
Computationally Intensive

scholarly journals Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 341
Author(s):  
Bugra Alkan ◽  
Malarvizhi Kaniappan Chinnathai
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Computation Time ◽  
Population Based ◽  
Performance Comparison ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Optimisation Algorithms

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

Pareto Front Estimation for Decision Making

2014 ◽  
Vol 22 (4) ◽  
pp. 651-678 ◽  
Author(s):  
Ioannis Giagkiozis ◽  
Peter J. Fleming
Keyword(s):  
Pareto Front ◽  
Computational Cost ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Number Of Solutions ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Problem Instances ◽  
Optimal Set ◽  
Optimisation Algorithms

The set of available multi-objective optimisation algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However, this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult—mainly due to the computational cost—to use a population large enough to ensure the likelihood of obtaining a solution close to the DM's preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimisation algorithm for two-objective and three-objective problem instances.

On the performance comparison of multi-objective evolutionary UAV path planners

2013 ◽  
Vol 238 ◽  
pp. 111-125 ◽  
Author(s):  
Eva Besada-Portas ◽  
Luis de la Torre ◽  
Alejandro Moreno ◽  
José L. Risco-Martín
Keyword(s):  
Performance Comparison ◽  
Multi Objective
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