An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental Selection
Multi-objective evolutionary algorithms (MOEAs) have received immense recognition due to their effectiveness and efficiency in tackling multi-objective optimization problems (MOPs). Recently, numerous studies on MOEAs revealed that when handling many-objective optimization problems (MaOPs) that have...
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doaj-c27b80d1ac62499ea96ea9dd63306d842021-03-30T01:43:21ZengIEEEIEEE Access2169-35362020-01-018827818279610.1109/ACCESS.2020.29917529084151An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental SelectionVikas Palakonda0Rammohan Mallipeddi1https://orcid.org/0000-0001-9071-1145School of Electronics Engineering, Kyungpook National University, Daegu, South KoreaDepartment of Artificial Intelligence, School of Electronics Engineering, Kyungpook National University, Daegu, South KoreaMulti-objective evolutionary algorithms (MOEAs) have received immense recognition due to their effectiveness and efficiency in tackling multi-objective optimization problems (MOPs). Recently, numerous studies on MOEAs revealed that when handling many-objective optimization problems (MaOPs) that have more than three objectives, MOEAs encounter challenges and the behavior of MOEAs resembles a random walk in search space as the proportion of nondominated solutions increases subsequently. This phenomenon is commonly observed in most classical Pareto-dominance-based MOEAs (PDMOEAs) such as NSGA-II, SPEAII, as these algorithms face difficulties in guiding the search process towards the optimal Pareto front due to lack of selection pressure. From the literature, it is evident that incorporating sum of normalized objectives into the framework of MOEAs would enhance the converging capabilities. Hence, in this work, we propose a novel multi-objective optimization algorithm with adaptive mating and environmental selection (ad-MOEA) which effectively incorporates the concept of sum of objectives in the mechanisms of mating and environmental selection to control the convergence and diversity adaptively. To demonstrate the effectiveness of the proposed ad-MOEA, we have conducted experiments on 26 test problems that includes DTLZ, WFG and MaOP test suites. Along with the benchmark problem, we have analyzed the performance of the proposed approach on real-world problems. The experimental results demonstrate the effectiveness of the proposed method with respect to the state-of-art methods.https://ieeexplore.ieee.org/document/9084151/Evolutionary computationmulti-objective optimizationmany-objective optimizationPareto-dominancesum of normalized objectivescrowding distance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vikas Palakonda Rammohan Mallipeddi |
spellingShingle |
Vikas Palakonda Rammohan Mallipeddi An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental Selection IEEE Access Evolutionary computation multi-objective optimization many-objective optimization Pareto-dominance sum of normalized objectives crowding distance |
author_facet |
Vikas Palakonda Rammohan Mallipeddi |
author_sort |
Vikas Palakonda |
title |
An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental Selection |
title_short |
An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental Selection |
title_full |
An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental Selection |
title_fullStr |
An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental Selection |
title_full_unstemmed |
An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental Selection |
title_sort |
evolutionary algorithm for multi and many-objective optimization with adaptive mating and environmental selection |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Multi-objective evolutionary algorithms (MOEAs) have received immense recognition due to their effectiveness and efficiency in tackling multi-objective optimization problems (MOPs). Recently, numerous studies on MOEAs revealed that when handling many-objective optimization problems (MaOPs) that have more than three objectives, MOEAs encounter challenges and the behavior of MOEAs resembles a random walk in search space as the proportion of nondominated solutions increases subsequently. This phenomenon is commonly observed in most classical Pareto-dominance-based MOEAs (PDMOEAs) such as NSGA-II, SPEAII, as these algorithms face difficulties in guiding the search process towards the optimal Pareto front due to lack of selection pressure. From the literature, it is evident that incorporating sum of normalized objectives into the framework of MOEAs would enhance the converging capabilities. Hence, in this work, we propose a novel multi-objective optimization algorithm with adaptive mating and environmental selection (ad-MOEA) which effectively incorporates the concept of sum of objectives in the mechanisms of mating and environmental selection to control the convergence and diversity adaptively. To demonstrate the effectiveness of the proposed ad-MOEA, we have conducted experiments on 26 test problems that includes DTLZ, WFG and MaOP test suites. Along with the benchmark problem, we have analyzed the performance of the proposed approach on real-world problems. The experimental results demonstrate the effectiveness of the proposed method with respect to the state-of-art methods. |
topic |
Evolutionary computation multi-objective optimization many-objective optimization Pareto-dominance sum of normalized objectives crowding distance |
url |
https://ieeexplore.ieee.org/document/9084151/ |
work_keys_str_mv |
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