Multi-Objective Evolutionary Algorithms: Past, Present, and Future

Coello, Carlos A. Coello; Brambila, Silvia González; Gamboa, Josué Figueroa; Tapia, Ma. Guadalupe Castillo

doi:10.1007/978-3-030-66515-9_5

Cited by 7 publications

(3 citation statements)

References 118 publications

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“…The field of MO metaheuristics is huge. The repository of publications and software dedicated solely to Evolutionary Algorithms [318] contains more than 10,000 books, articles, presentations, dissertations and software items. The interested reader is also directed to a book on MO Evolutionary Algorithms [319] with its in-depth approach.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Jakšić

2024

Photonics

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The interplay between two paradigms, artificial intelligence (AI) and optical metasurfaces, nowadays appears obvious and unavoidable. AI is permeating literally all facets of human activity, from science and arts to everyday life. On the other hand, optical metasurfaces offer diverse and sophisticated multifunctionalities, many of which appeared impossible only a short time ago. The use of AI for optimization is a general approach that has become ubiquitous. However, here we are witnessing a two-way process—AI is improving metasurfaces but some metasurfaces are also improving AI. AI helps design, analyze and utilize metasurfaces, while metasurfaces ensure the creation of all-optical AI chips. This ensures positive feedback where each of the two enhances the other one: this may well be a revolution in the making. A vast number of publications already cover either the first or the second direction; only a modest number includes both. This is an attempt to make a reader-friendly critical overview of this emerging synergy. It first succinctly reviews the research trends, stressing the most recent findings. Then, it considers possible future developments and challenges. The author hopes that this broad interdisciplinary overview will be useful both to dedicated experts and a general scholarly audience.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Jakšić

2024

Photonics

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“…The area of metaheuristics-based multi-objective optimization is enormously vast. In [ 182 ], it is mentioned that the EMOO repository (its web address is given in the reference quoted in the previous sentence), related solely to a single type of multi-objective optimization (the evolutionary algorithms), contains over 12,400 bibliographic references alone (publications and software).…”

Section: Multi-objective Optimization (Moo)mentioning

confidence: 99%

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Jakšić

Devi²,

Jakšić

et al. 2023

Biomimetics

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The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

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“…Therefore, it can be transformed into a sequence of multi-objective optimization problems (MOPs) with constraints (Ma et al, 2021a;Kumar et al, 2021). When solving MOPs, researchers have proposed multi-objective evolutionary algorithms (MOEA) to find their true Pareto front (PF) (Ma et al, 2021b;Coello et al, 2021). With MOEAs, multiple models can be obtained in one optimization, not just a single solution.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cooperation Multi-Objective Optimization Approach: Multi-Swarm Multi-Objective Evolutionary Algorithm Based on Decomposition (MSMOEA/D)

Chen

Wang

et al. 2022

Front. Energy Res.

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In order to achieve good adaptability, medical bone implants for clinical applications need to have porous characteristics. From a biological and mechanical point of view, the design of porous structures requires both suitable porosities to facilitate cell ingrowth and suitable strength to avoid implant damage. To handle the multiobjective optimization problems of porous structure design, this work introduced an improved multi-objective optimization algorithm, which is called a multi-swarm multi-objective evolutionary algorithm based on decomposition (MSMOEA/D), and the main idea is a multi-swarm strategy. After a predetermined algebraic evolution, the whole swarm was evenly divided into several parts, and the elite non-dominated sorting mechanism was used to select the individuals with excellent performance and poor performance in the sub-swarms to exchange information between the sub-swarms. The performance of the MSMOEA/D algorithm was verified and validated on 12 constraint two-objective and three-objective benchmark functions and compared with MOEA/D, MOEADM2M, and MOEADDRA algorithms in terms of generational distance indicators. The solutions obtained by the proposed MSMOEA/D algorithm were accurate. Finally, the proposed MSMOEA/D algorithm was applied to optimize the constructed RS porous structure, and the porous optimized models with porosities of 50%, 60% and 70% were obtained.

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Multi-Objective Evolutionary Algorithms: Past, Present, and Future

Cited by 7 publications

References 118 publications

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

A Novel Cooperation Multi-Objective Optimization Approach: Multi-Swarm Multi-Objective Evolutionary Algorithm Based on Decomposition (MSMOEA/D)

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