Pareto Dominance Based Multi-objective Cohort Intelligence Algorithm

2020 ◽  
Author(s):  
Mukundraj Patil ◽  
Anand Kulkarni
Keyword(s):  
Pareto Dominance ◽  
Multi Objective ◽  
Intelligence Algorithm

Pareto Set as a Model for Dispatching Resources in Emergency Centres (Preprint)

2018 ◽  
Author(s):  
Ricardo Guedes ◽  
Vasco Furtado ◽  
Tarcísio Pequeno ◽  
Joel Rodrigues
Keyword(s):  
Operational Research ◽  
Real Data ◽  
Pareto Set ◽  
Multi Objective Optimization ◽  
Pareto Dominance ◽  
Four Dimensions ◽  
Multi Objective ◽  
The Best Approximation ◽  
One Year ◽  
The Cost

UNSTRUCTURED The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them.

An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance

2021 ◽  
Author(s):  
Zhigang Lu ◽  
Shengjing Qi ◽  
Jiangfeng Zhang ◽  
Yao Cai ◽  
Xiaoqiang Guo ◽  
...  
Keyword(s):  
Pareto Dominance ◽  
Bacterial Colony ◽  
Multi Objective

scholarly journals A Multi-objective Version of the Lin-Kernighan Heuristic for the Traveling Salesman Problem

2018 ◽  
Vol 25 (1) ◽  
pp. 48
Author(s):  
Emerson Bezerra De Carvalho ◽  
Elizabeth Ferreira Gouvêa Goldbarg ◽  
Marco Cesar Goldbarg
Keyword(s):  
Local Search ◽  
Traveling Salesman Problem ◽  
State Of The Art ◽  
Symmetric Case ◽  
Traveling Salesman ◽  
Weighted Sum ◽  
Pareto Dominance ◽  
Multi Objective ◽  
The Traveling Salesman Problem ◽  
Single Objective

The Lin and Kernighan’s algorithm for the single objective Traveling Salesman Problem (TSP) is one of the most efficient heuristics for the symmetric case. Although many algorithms for the TSP were extended to the multi-objective version of the problem (MTSP), the Lin and Kernighan’s algorithm was still not fully explored. Works that applied the Lin and Kernighan’s algorithm for the MTSP were driven to weighted sum versions of the problem. We investigate the LK from a Pareto dominance perspective. The multi-objective LK was implemented within two local search schemes and applied to 2 to 4-objective instances. The results  showed that the proposed algorithmic variants obtained better results than a state-of-the-art algorithm.

Prediction of Pareto Dominance Using an Attribute Tendency Model for Expensive Multi-Objective Optimization

2019 ◽  
Vol 29 (02) ◽  
pp. 2050021
Author(s):  
Wenbin Li ◽  
Junqiang Jiang ◽  
Xi Chen ◽  
Guanqi Guo ◽  
Jianjun He
Keyword(s):  
Surrogate Model ◽  
Prediction Method ◽  
Optimization Methods ◽  
Black Box ◽  
Benchmark Problems ◽  
Pareto Dominance ◽  
Decision Space ◽  
Multi Objective ◽  
Decision Attributes ◽  
Coarse Model

This paper proposes a novel surrogate-assisted multi-objective evolutionary algorithm, MOEA-ATCM, to solve expensive or black-box multi-objective problems with small evaluation budgets. The proposed approach encompasses a state-of-the-art MOEA based on a nondominated sorting genetic algorithm assisted by multi-fidelity optimization methods. A high-fidelity attribute tendency (AT) surrogate model was used to construct a linear decision space by introducing the knowledge of the objective space. A coarse model (CM) based on the AT model and correlation analyses of the objective functions and decision attributes were used to predict the Pareto dominance for candidates in the new decision space constructed by the AT model. Two major roles of MOEA-ATCM were identified: (1) the development of a new multi-fidelity surrogate-model-based method to predict Pareto dominance in a decision space that was then applied to MOEA, which does not need to dynamically update surrogate models in the optimization process and (2) the development of a Pareto dominance prediction method to obtain good nondominated solutions of expensive or black box problems with relatively few objective function evaluations. The advantages of MOEA-ATCM were verified by mathematical benchmark problems and a real-world multi-objective parameter optimization problem.

Pareto dominance based Multiobjective Cohort Intelligence algorithm

2020 ◽  
Vol 538 ◽  
pp. 69-118 ◽  
Author(s):  
Mukundraj V. Patil ◽  
Anand J. Kulkarni
Keyword(s):  
Pareto Dominance ◽  
Intelligence Algorithm
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