18,102 research outputs found
Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search
The large-scale multiobjective optimization problem (LSMOP) is characterized
by simultaneously optimizing multiple conflicting objectives and involving
hundreds of decision variables. {Many real-world applications in engineering
fields can be modeled as LSMOPs; simultaneously, engineering applications
require insensitivity in performance.} This requirement usually means that the
results from the algorithm runs should not only be good for every run in terms
of performance but also that the performance of multiple runs should not
fluctuate too much, i.e., the algorithm shows good insensitivity. Considering
that substantial computational resources are requested for each run, it is
essential to improve upon the performance of the large-scale multiobjective
optimization algorithm, as well as the insensitivity of the algorithm. However,
existing large-scale multiobjective optimization algorithms solely focus on
improving the performance of the algorithms, leaving the insensitivity
characteristics unattended. {In this work, we propose an evolutionary algorithm
for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS,
which aims to improve the performance and insensitivity for large-scale
multiobjective optimization problems.} The proposed method samples the decision
variables to construct new nodes on the Monte Carlo tree for optimization and
evaluation. {It selects nodes with good evaluation for further search to reduce
the performance sensitivity caused by large-scale decision variables.} We
compare the proposed algorithm with several state-of-the-art designs on
different benchmark functions. We also propose two metrics to measure the
sensitivity of the algorithm. The experimental results confirm the
effectiveness and performance insensitivity of the proposed design for solving
large-scale multiobjective optimization problems.Comment: 12 pages, 11 figure
Explainable Bayesian Optimization
In industry, Bayesian optimization (BO) is widely applied in the human-AI
collaborative parameter tuning of cyber-physical systems. However, BO's
solutions may deviate from human experts' actual goal due to approximation
errors and simplified objectives, requiring subsequent tuning. The black-box
nature of BO limits the collaborative tuning process because the expert does
not trust the BO recommendations. Current explainable AI (XAI) methods are not
tailored for optimization and thus fall short of addressing this gap. To bridge
this gap, we propose TNTRules (TUNE-NOTUNE Rules), a post-hoc, rule-based
explainability method that produces high quality explanations through
multiobjective optimization. Our evaluation of benchmark optimization problems
and real-world hyperparameter optimization tasks demonstrates TNTRules'
superiority over state-of-the-art XAI methods in generating high quality
explanations. This work contributes to the intersection of BO and XAI,
providing interpretable optimization techniques for real-world applications
Multiobjective dynamic optimization of a fed-batch copolymerization reactor
Multiobjective optimization problems are encountered in most real-world applications and
more recently in chemical processes ([1], [2], [3], [4]). Since such problems involve several
objective functions with conflicting nature, the final optimum is not unique but a set of non
dominated solutions (the Pareto front) which show a trade-off among the whole objectives. A
decision support approach is then used to rank the Pareto solutions according to the decision
maker’s preferences
Capabilities of EMOA to detect and preserve equivalent Pareto subsets
Recent works in evolutionary multiobjective optimization suggest to shift the focus from solely evaluating optimization success in the objective space to also taking the decision space into account. They indicate that this may be a) necessary to express the users requirements of obtaining distinct solutions (distinct Pareto set parts or subsets) of similar quality (comparable locations on the Pareto front) in real-world applications, and b) a demanding task for the currently most commonly used algorithms.We investigate if standard EMOA are able to detect and preserve equivalent Pareto subsets and develop an own special purpose EMOA that meets these requirements reliably
Bat Algorithm for Multi-objective Optimisation
Engineering optimization is typically multiobjective and multidisciplinary
with complex constraints, and the solution of such complex problems requires
efficient optimization algorithms. Recently, Xin-She Yang proposed a
bat-inspired algorithm for solving nonlinear, global optimisation problems. In
this paper, we extend this algorithm to solve multiobjective optimisation
problems. The proposed multiobjective bat algorithm (MOBA) is first validated
against a subset of test functions, and then applied to solve multiobjective
design problems such as welded beam design. Simulation results suggest that the
proposed algorithm works efficiently.Comment: 12 pages. arXiv admin note: text overlap with arXiv:1004.417
Multiobjective synchronization of coupled systems
Copyright @ 2011 American Institute of PhysicsSynchronization of coupled chaotic systems has been a subject of great interest and importance, in theory but also various fields of application, such as secure communication and neuroscience. Recently, based on stability theory, synchronization of coupled chaotic systems by designing appropriate coupling has been widely investigated. However, almost all the available results have been focusing on ensuring the synchronization of coupled chaotic systems with as small coupling strengths as possible. In this contribution, we study multiobjective synchronization of coupled chaotic systems by considering two objectives in parallel, i. e., minimizing optimization of coupling strength and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach. The constraints on the coupling form are also investigated by formulating the problem into a multiobjective constraint problem. We find that the proposed evolutionary method can outperform conventional adaptive strategy in several respects. The results presented in this paper can be extended into nonlinear time-series analysis, synchronization of complex networks and have various applications
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
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