Solving multi-objective optimization problems using self-adaptive harmony search algorithms

2019 ◽  
Vol 24 (6) ◽  
pp. 4081-4107
Author(s):  
Yin-Fu Huang ◽  
Sih-Hao Chen
Keyword(s):  
Optimization Problems ◽  
Harmony Search ◽  
Search Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Self Adaptive

scholarly journals A Novel Multi-Objective Harmony Search Algorithm with Pitch Adjustment by Genotype

2021 ◽  
Vol 11 (19) ◽  
pp. 8931
Author(s):  
Daniel Molina-Pérez ◽  
Edgar Alfredo Portilla-Flores ◽  
Eduardo Vega-Alvarado ◽  
Maria Bárbara Calva-Yañez ◽  
Gabriel Sepúlveda-Cervantes
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Fronts ◽  
Exploration Exploitation ◽  
Pitch Adjustment

In this work, a new version of the Harmony Search algorithm for solving multi-objective optimization problems is proposed, MOHSg, with pitch adjustment using genotype. The main contribution consists of adjusting the pitch using the crowding distance by genotype; that is, the distancing in the search space. This adjustment automatically regulates the exploration–exploitation balance of the algorithm, based on the distribution of the harmonies in the search space during the formation of Pareto fronts. Therefore, MOHSg only requires the presetting of the harmony memory accepting rate and pitch adjustment rate for its operation, avoiding the use of a static bandwidth or dynamic parameters. MOHSg was tested through the execution of diverse test functions, and it was able to produce results similar or better than those generated by algorithms that constitute search variants of harmonies, representative of the state-of-the-art in multi-objective optimization with HS.

scholarly journals A self-adaptive population Rao algorithm for optimization of selected bio-energy systems

2020 ◽  
Author(s):  
R Venkata Rao ◽  
Hameer Singh Keesari
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Energy Systems ◽  
Design Parameters ◽  
Perturbation Equation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fitness Value ◽  
Self Adaptive

Abstract This work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups. For each sub-population, a unique perturbation equation is randomly allocated. Each perturbation equation guides the solutions toward different regions of the search space. The performance of the proposed algorithm is examined using standard optimization benchmark problems having different characteristics in the single- and multi-objective optimization scenarios. The results of the application of the proposed algorithm are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed method are superior. Furthermore, the proposed algorithm is used to identify optimum design parameters through multi-objective optimization of a fertilizer-assisted microalgae cultivation process and many-objective optimization of a compression ignition biodiesel engine system. From the results of the computational tests, it is observed that the performance of the self-adaptive population Rao algorithm is superior or competitive to the other advanced optimization algorithms. The performances of the considered bio-energy systems are improved by the application of the proposed optimization algorithm. The proposed optimization algorithm is more robust and may be easily extended to solve single-, multi-, and many-objective optimization problems of different science and engineering disciplines.

METCO: A PARALLEL PLUGIN-BASED FRAMEWORK FOR MULTI-OBJECTIVE OPTIMIZATION

2009 ◽  
Vol 18 (04) ◽  
pp. 569-588 ◽  
Author(s):  
COROMOTO LEÓN ◽  
GARA MIRANDA ◽  
CARLOS SEGURA
Keyword(s):  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Test Problems ◽  
Multi Objective Optimization ◽  
End User ◽  
Multi Objective ◽  
Adaptive Models ◽  
Real World Applications ◽  
Computational Resources ◽  
Self Adaptive

This paper presents a parallel framework for the solution of multi-objective optimization problems. The framework implements some of the best known multi-objective evolutionary algorithms. The plugin-based architecture of the framework minimizes the end user effort required to incorporate their own problems and evolutionary algorithms, and facilitates tool maintenance. A wide variety of configuration options can be specified to adapt the software behavior to many different parallel models. An innovation of the framework is that it provides a self-adaptive parallel model that is based on the cooperation of a set of evolutionary algorithms. The aim of the new model is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. The model proposed is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach. The model grants more computational resources to those algorithms that show a more promising behavior. The flexibility and efficiency of the framework were tested and demonstrated by configuring standard and self-adaptive models for test problems and real-world applications.

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