Applying Social Choice Theory to Solve Engineering Multi-objective Optimization Problems

Carvalho, Vinicius Renan de; Larson, Kate; Brandão, Anarosa Alves Franco; Sichman, Jaime Simão

doi:10.1007/s40313-019-00526-2

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…As for the experimental evaluation, we considered as LLHs three MOEAs: NSGA-II [18], IBEA [19], and SPEA2 [20]. We chose these MOEAs due to their popularity and also because several other selection hyper-heuristics [13,15,16] have made use of them, and so this is more suitable for the analysis. Selection of individuals was binary tournament, we used Simulated Binary Crossover (SBX) [61] with probability 0.9 and distribution index 20, and Polynomial mutation with probability 1/n (n = number of parameters) and distribution index 20.…”

Section: Resultsmentioning

confidence: 99%

“…MOABHH is an agent-based hyper-heuristic framework focused on online selection by means of voting techniques [15]. The main idea is to consider MOEAs as candidates and quality indicators as voters in an election.…”

Section: Related Workmentioning

confidence: 99%

“…However, the use of population-based methods is a recently growing area of research [9] where hyper-heuristics for multi-objective optimisation are employed either for controlling the own components of Multi-Objective Evolutionary Algorithms (MOEAs) [10,11,12], e.g. mutation and crossover operators as LLHs, or the LLHs being complete MOEAs [13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

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Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance

Júnior

Özcan

Carvalho

2020

Applied Soft Computing

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance

Júnior

Özcan

Carvalho

2020

Applied Soft Computing

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“…In the latter [29], they evaluated how MOABHH performs on real-world problems, including the Crashworthiness, Water [33], Car Side Impact [34], and Machining [35]. In [36], the authors evaluated the proposed approach considering different voting criterion considering these four real-world problems and the multi-objective travel salesperson problem [37]. This proposed HH outperformed each individual MOEA and random choice hyper-heuristic; however, MOABHH was not compared to any of the other state-of-the-art hyper-heuristics.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Selection Hyper-Heuristics for Real-World Multi-Objective Optimization Problems

2021

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As exact algorithms are unfeasible to solve real optimization problems, due to their computational complexity, meta-heuristics are usually used to solve them. However, choosing a meta-heuristic to solve a particular optimization problem is a non-trivial task, and often requires a time-consuming trial and error process. Hyper-heuristics, which are heuristics to choose heuristics, have been proposed as a means to both simplify and improve algorithm selection or configuration for optimization problems. This paper novel presents a novel cross-domain evaluation for multi-objective optimization: we investigate how four state-of-the-art online hyper-heuristics with different characteristics perform in order to find solutions for eighteen real-world multi-objective optimization problems. These hyper-heuristics were designed in previous studies and tackle the algorithm selection problem from different perspectives: Election-Based, based on Reinforcement Learning and based on a mathematical function. All studied hyper-heuristics control a set of five Multi-Objective Evolutionary Algorithms (MOEAs) as Low-Level (meta-)Heuristics (LLHs) while finding solutions for the optimization problem. To our knowledge, this work is the first to deal conjointly with the following issues: (i) selection of meta-heuristics instead of simple operators (ii) focus on multi-objective optimization problems, (iii) experiments on real world problems and not just function benchmarks. In our experiments, we computed, for each algorithm execution, Hypervolume and IGD+ and compared the results considering the Kruskal–Wallis statistical test. Furthermore, we ranked all the tested algorithms considering three different Friedman Rankings to summarize the cross-domain analysis. Our results showed that hyper-heuristics have a better cross-domain performance than single meta-heuristics, which makes them excellent candidates for solving new multi-objective optimization problems.

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Optimising multi-value alignment: a multi-objective evolutionary strategy for normative multi-agent systems

Riad,

de Carvalho,

Golpayegani

2024

Neural Comput & Applic

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Applying Social Choice Theory to Solve Engineering Multi-objective Optimization Problems

Cited by 10 publications

References 26 publications

Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance

Hyper-Heuristics based on Reinforcement Learning, Balanced Heuristic Selection and Group Decision Acceptance

Comparative Analysis of Selection Hyper-Heuristics for Real-World Multi-Objective Optimization Problems

Optimising multi-value alignment: a multi-objective evolutionary strategy for normative multi-agent systems

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