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Hybridizing differential evolution and novelty search for multimodal optimization problems

Published: 13 July 2019 Publication History

Abstract

Multimodal optimization has shown to be a complex paradigm underneath real-world problems arising in many practical applications, with particular prevalence in physics-related domains. Among them, a plethora of cases within the computational design of aerospace structures can be modeled as a multimodal optimization problem, such as aerodynamic optimization or airfoils and wings. This work aims at presenting a new research direction towards efficiently tackling this kind of optimization problems, which pursues the discovery of the multiple (at least locally optimal) solutions of a given optimization problem. Specifically, we propose to exploit the concept behind the so-called Novelty Search mechanism and embed it into the self-adaptive Differential Evolution algorithm so as to gain an increased level of controlled diversity during the search process. We assess the performance of the proposed solver over the well-known CEC'2013 suite of multimodal test functions. The obtained outcomes of the designed experimentation supports our claim that Novelty Search is a promising approach for heuristically addressed multimodal problems.

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  • (2023)Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in DronesDrones10.3390/drones70704277:7(427)Online publication date: 27-Jun-2023
  • (2022)Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation ProblemsEvolutionary Computation in Combinatorial Optimization10.1007/978-3-031-04148-8_8(111-126)Online publication date: 20-Apr-2022
  • (2021)Novelty Search in Particle Swarm Optimization2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9660131(1-8)Online publication date: 5-Dec-2021
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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 July 2019

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Author Tags

  1. differential evolution
  2. multimodal optimization
  3. novelty search

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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View all
  • (2023)Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in DronesDrones10.3390/drones70704277:7(427)Online publication date: 27-Jun-2023
  • (2022)Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation ProblemsEvolutionary Computation in Combinatorial Optimization10.1007/978-3-031-04148-8_8(111-126)Online publication date: 20-Apr-2022
  • (2021)Novelty Search in Particle Swarm Optimization2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9660131(1-8)Online publication date: 5-Dec-2021
  • (2021)Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challengesInformation Fusion10.1016/j.inffus.2020.10.01467(161-194)Online publication date: Mar-2021

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