Multi-Objective Binary Fish School Search

Fish school search (FSS) algorithm has inspired several adaptations for multi-objective problems or binary optimization. However, there is no particular proposition to solve both problems simultaneously. The proposed multi-objective approach binary fish school search (MOBFSS) aims to solve optimization problems with two or three conflicting objective functions with binary decision input variables. MOBFSS is based on the dominance concept used in the multi-objective fish school search (MOFSS) and the threshold technique deployed in the binary fish school search (BFSS). Additionally, the authors evaluate the proposal for feature selection for classification in well-known datasets. Moreover, the authors compare the performance of the proposal with a state-of-art algorithm called BMOPSO-CDR. MOBFSS presents better results than BMOPSO-CDR, especially for datasets with higher complexity.

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A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

Mathematical and Computational Applications ◽

10.3390/mca26020027 ◽

2021 ◽

Vol 26 (2) ◽

pp. 27

Author(s):

Alejandro Castellanos-Alvarez ◽

Laura Cruz-Reyes ◽

Eduardo Fernandez ◽

Nelson Rangel-Valdez ◽

Claudia Gómez-Santillán ◽

...

Keyword(s):

Genetic Algorithm ◽

Selective Pressure ◽

Optimization Problems ◽

Region Of Interest ◽

Decision Makers ◽

Multiple Objective ◽

Objective Functions ◽

Multi Objective ◽

Imperfect Knowledge ◽

Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

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Sparsity-based evolutionary multi-objective feature selection for multi-label classification

Proceedings of the Genetic and Evolutionary Computation Conference Companion ◽

10.1145/3449726.3459467 ◽

2021 ◽

Author(s):

Kaan Demir ◽

Bach Hoai Nguyen ◽

Bing Xue ◽

Mengjie Zhang

Keyword(s):

Feature Selection ◽

Multi Objective ◽

Selection For

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Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.984-985.419 ◽

2014 ◽

Vol 984-985 ◽

pp. 419-424

Author(s):

P. Sabarinath ◽

M.R. Thansekhar ◽

R. Saravanan

Keyword(s):

Design Optimization ◽

Optimization Problems ◽

Optimization Methods ◽

Optimal Solutions ◽

Objective Functions ◽

Nsga Ii ◽

Multi Objective ◽

Total Displacement ◽

Design Variables ◽

Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

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A Particle Swarm Optimisation Based Multi-objective Filter Approach to Feature Selection for Classification

Lecture Notes in Computer Science - PRICAI 2012: Trends in Artificial Intelligence ◽

10.1007/978-3-642-32695-0_59 ◽

2012 ◽

pp. 673-685 ◽

Cited By ~ 7

Author(s):

Bing Xue ◽

Liam Cervante ◽

Lin Shang ◽

Mengjie Zhang

Keyword(s):

Feature Selection ◽

Particle Swarm ◽

Particle Swarm Optimisation ◽

Multi Objective ◽

Selection For ◽

Filter Approach

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Parameter Selection for Swarm Intelligence Algorithms

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2018100101 ◽

2018 ◽

Vol 9 (4) ◽

pp. 1-20

Author(s):

Breno A. M. Menezes ◽

Fabian Wrede ◽

Herbert Kuchen ◽

Fernando B. Lima Neto

Keyword(s):

Population Size ◽

Swarm Intelligence ◽

Execution Time ◽

Optimization Problems ◽

Parallel Implementation ◽

Fish School ◽

Size Number ◽

Complex Optimization ◽

Selection For ◽

Number Of Iterations

Swarm intelligence (SI) algorithms are handy tools for solving complex optimization problems. When problems grow in size and complexity, an increase in population or number of iterations might be required in order to achieve a good solution. These adjustments also impact the execution time. This article investigates the trade-off involving population size, number of iterations and problem complexity, aiming to improve the efficiency of SI algorithms. Results based on a parallel implementation of Fish School Search show that increasing the population size is beneficial for finding good solutions. However, we observed an asymptotic behavior, i.e. increasing the population over a certain threshold only leads to slight improvements. Furthermore, the execution time was analyzed.

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