Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art

Raquel, Carlo; Yao, Xin

doi:10.1007/978-3-642-38416-5_4

Cited by 50 publications

(19 citation statements)

References 28 publications

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“…Type 2 methods include "dynamic MOO" -a hot research topic in computer science [54][55][56], although it is seldom applied in conceptual architectural design. This topic aims at developing methods to solve dynamic MOO problems which involve time-varying objectives, constraints and design variables, such as control problems, scheduling problems, mechanical design problems, etc.…”

Section: Type 1 Methods With Non-dynamic and Non-interactive Re-opfmentioning

confidence: 99%

Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Ding

Stefano²,

Turrin

et al. 2020

Automation in Construction

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Simulation-Based Multi-Objective Optimization (SBMOO) methods are being increasingly used in conceptual architectural design. They mostly focus on the solving, rather than the re-formulation, of a Multi-Objective Optimization (MOO) problem. However, Optimization Problem Re-Formulation (Re-OPF) is necessary for treating ill-defined conceptual architectural design as an iterative exploration process. The paper proposes an innovative SBMOO method which builds in a dynamic and interactive Re-OPF phase. This Re-OPF phase, as the main novelty of the proposed method, aims at achieving a realistic MOO model (i.e., a parametric geometrysimulation model which includes important objectives, constraints, and design variables). The proposed method is applied to the conceptual design of a top-daylighting system, focusing on divergent concept generation. The integration of software tools Grasshopper and modeFRONTIER is adopted to support this application. The main finding from this application is that the proposed method can help to achieve quantitatively better and qualitatively more diverse Pareto solutions.

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Section: Type 1 Methods With Non-dynamic and Non-interactive Re-opfmentioning

confidence: 99%

Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Ding

Stefano²,

Turrin

et al. 2020

Automation in Construction

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“…Much progress [36,40,2,20] has been made in the DMOPs field in recent years, and most existing algorithms can be classified into the following categories: Increasing/Maintaining Diversity methods, Memory based methods, Multi-population based methods, and Prediction based methods.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms

Jiang

Huang²,

Qiu

et al. 2018

IEEE Trans. Evol. Computat.

304

116

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One of the major distinguishing features of the Dynamic Multiobjective Optimization Problems (DMOPs) is that optimization objectives will change over time, thus tracking the varying Pareto-Optimal Front (POF) becomes a challenge. One of the promising solutions is reusing "experiences" to construct a prediction model via statistical machine learning approaches. However, most existing methods neglect the non-independent and identically distributed nature of data to construct the prediction model. In this paper, we propose an algorithmic framework, called Tr-DMOEA, which integrates transfer learning and population-based evolutionary algorithms (EAs) to solve the DMOPs. This approach exploits the transfer learning technique as a tool to generate an effective initial population pool via reusing past experience to speed up the evolutionary process, and at the same time any population based multiobjective algorithms can benefit from this integration without any extensive modifications. To verify this idea, we incorporate the proposed approach into the development of three well-known evolutionary algorithms, nondominated sorting genetic algorithm II (NSGA-II), multiojective particle swarm 1 arXiv:1612.06093v2 [cs.NE] 18 Nov 2017 optimization (MOPSO), and the regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA). We employ twelve benchmark functions to test these algorithms as well as compare them with some chosen state-of-the-art designs. The experimental results confirm the effectiveness of the proposed design for DMOPs.

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“…Therefore, the dynamic multi-objective optimisation problem in the finite period [1, ] can be defined as in Equation 9 (Raquel and Yao, 2013):…”

Section: Defining the Dynamics Of The Dmopmentioning

confidence: 99%

Dynamic multi-objective optimisation using deep reinforcement learning: benchmark, algorithm and an application to identify vulnerable zones based on water quality

Hasan

Lwin

Imani

et al. 2019

Engineering Applications of Artificial Intelligence

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Dynamic multi-objective optimisation problem (DMOP) has brought a great challenge to the reinforcement learning (RL) research area due to its dynamic nature such as objective functions, constraints and problem parameters that may change over time. This study aims to identify the lacking in the existing benchmarks for multi-objective optimisation for the dynamic environment in the RL settings. Hence, a dynamic multi-objective testbed has been created which is a modified version of the conventional deep-sea treasure (DST) hunt testbed. This modified testbed fulfils the changing aspects of the dynamic environment in terms of the characteristics where the changes occur based on time. To the authors' knowledge, this is the first dynamic multi-objective testbed for RL research, especially for deep reinforcement learning. In addition to that, a generic algorithm is proposed to solve the multi-objective optimisation problem in a dynamic constrained environment that maintains equilibrium by mapping different objectives simultaneously to provide the most compromised solution that closed to the true Pareto front (PF). As a proof of concept, the developed algorithm has been implemented to build an expert system for a real-world scenario using Markov decision process to identify the vulnerable zones based on water quality resilience in São Paulo, Brazil. The outcome of the implementation reveals that the proposed parity-Q deep Q network (PQDQN) algorithm is an efficient way to optimise the decision in a dynamic environment. Moreover, the result shows PQDQN algorithm performs better compared to the other state-of-the-art solutions both in the simulated and the real-world scenario.

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Dynamic Multi-objective Optimization: A Survey of the State-of-the-Art

Cited by 50 publications

References 28 publications

Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms

Dynamic multi-objective optimisation using deep reinforcement learning: benchmark, algorithm and an application to identify vulnerable zones based on water quality

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