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for solving a specific optimization problem. ... For each optimization method, we surveyed the various algorithms in literature that pertain to the method. ... Peitz and Dellnitz  surveyed multi-objective optimal control methods that solve complex problems. ...doi:10.1109/access.2020.2989219 fatcat:sxcq3y5ijvbb5euvjfqdgjbbxi
Multi-objective evolutionary algorithm (MOEA) is the main method to solve multi-objective optimization problem (MOP), which has become one of the hottest research areas of evolutionary computation. ... Finally several viewpoints for the future research of MOEA are presented. ... MOEA/D is difficult to choose a suitable decomposition method for different problems. ...doi:10.1088/1742-6596/1288/1/012057 fatcat:jugqqqyonvhoflgkvs5xmdtjga
Since the search for multiple solutions is important in multi-objective optimization, a detailed description of EAs, especially designed to solve multi-modal opti- mization problems, is also presented. ... For those readers unfamiliar with multi- objective optimization, Chapters 2 and 3 provide the necessary background. ...
Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multiple conflicting objective functions. ... They aim at finding a set of representative Pareto optimal solutions in a single run. ... This paper provides a state-of-the-art survey of MOEAs that employ indicator and decomposition functions for guiding their search and evolve their populations. ...doi:10.14569/ijacsa.2016.070274 fatcat:3oleqyfntzdz5hwkd3f5df56qi
Then the multi-object control problem is formulated as an optimization problem for a vector functional, leading to the optimality condition. ... We give a survey of papers devoted to strict factorization and decomposition of controlled dynamical systems.” ...
This paper is the second part of a two-part survey series on large-scale global optimization. ... We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, ... INTRODUCTION The first part of this two-part survey series covered decomposition methods and hybrid methods as two most widely investigated approaches in the literature. ...doi:10.1109/tevc.2021.3130835 fatcat:3x5vho5kxfg4tc7zf4zd4c3crm
Specifically, the method has been generalized to broad classes of problems (i.e.,multi-block, coupled objective, nonconvex, etc.). ... The well-known alternating direction method of multipliers (ADMM) has turned out to be one of the most popular tools for distributed optimization due to many advantages, such as modular structure, superior ... For the coupled objective component 𝑔, a Gauss-Seidel decomposition is equivalent to a block coordinate method. ...arXiv:2208.03700v2 fatcat:fayuks2ucvbavckoqk4x3ywc3m
Despotis, Sotiros, and Koronakos (2016c) , based on the composition paradigm, developed a multi-objective programming approach that employs a min-max optimization model. ... a multi-objective programming framework, differentiating only in the definition of the overall system efficiency and the solution procedure adopted. ... in a multi-objective programming framework. ...doi:10.1016/j.ejor.2018.04.004 fatcat:tamhdp65czgzxlxetlvfraebrm
SN Applied Sciences
In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions. ... This paper provides a detailed analysis and comparison of the recent research work in this area, and concludes with a critical analysis of the field, and future research perspectives. ... (Multi Objective Optimization Problem MOP). ...doi:10.1007/s42452-019-0872-y fatcat:y6hkwvnapnfpnax3k7xnqkj2gi
Many-objective optimization brings with it a number of challenges that must be addressed, which highlights the need for new and better algorithms that can efficiently handle the growing number of objectives ... Multi-objective optimization problems having more than three objectives are referred to as many-objective optimization problems. ... There, the authors investigate whether there is an advantage of using a decomposition-based method over Pareto-based methods. ...doi:10.1016/j.sorms.2015.08.001 fatcat:uzeelz53dff6hdi6e357hjxx6m
Abhijit Chavan for extending his kind help to carry out the experimentation. ... Acknowledgement: The authors would like to thank the editorial team and anonymous reviewers for helping us to enhance the article's quality and contribution with their suggestions. ... Marler and Arora  presented a survey about 'multi-objective optimization methods' developed to address multi-objective engineering problems. ...doi:10.24200/sci.2021.53026.3017 fatcat:jq7jcrciwbgevlikxlqdwfu4bm
We propose a modular framework for multi-relational learning via tensor decomposition. ... We learn this latent representation by computing a low-rank tensor decomposition, using quasi-Newton optimization of a weighted objective function. ... In this paper, we propose a tensor decomposition model for transduction on multi-relational data. ...arXiv:1303.1733v2 fatcat:2hvv6pp4tvhs7hdxqgczc5hjd4
These techniques consist of decomposition methods such as the generalized Benders decomposition (GBD) and the branch-and-bound enumerative algorithm. ... Moreover, high dimensional real-world models and combinatorial alternatives require adequate tools for large-scale optimization models. ... We can mention two other aspects such as the use of multi-objective optimization methods in a fuzzy environment  and the convexification methods adapted to relaxation procedures  . ...doi:10.5963/jwrhe0402003 fatcat:am7eqcua2rfu5byoei7wv6i3ju
Decomposition techniques that are widely used in multi-objective evolutionary optimization require a set of evenly distributed weight vectors to generate a diverse set of solutions on the Pareto-optimal ... In this paper, a user-preference based evolutionary multi-objective algorithm is proposed that uses decomposition methods for solving many-objective problems. ... Another approach for better handling many-objective optimization problems is using decomposition-based methods. They convert a multi-objective problem into a set of singleobjective problems. ...doi:10.1109/cec.2014.6900595 dblp:conf/cec/MohammadiOLD14 fatcat:2yimzf7hvzgf5jdzy3fvdk4g4q
This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. ... Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research. ... Conflicts of Interest: The authors declare no conflict of interest. ...doi:10.3390/s22031235 pmid:35161979 pmcid:PMC8839296 fatcat:fhi5nbxz6vfa7h3qw7cjs667bq
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