962 research outputs found
Generalized multiobjective evolutionary algorithm guided by descent directions
This paper proposes a generalized descent directions-guided multiobjective algorithm
(DDMOA2). DDMOA2 uses the scalarizing fitness assignment in its parent and
environmental selection procedures. The population consists of leader and non-leader individuals.
Each individual in the population is represented by a tuple containing its genotype
as well as the set of strategy parameters. The main novelty and the primary strength of
our algorithm is its reproduction operator, which combines the traditional local search
and stochastic search techniques. To improve efficiency, when the number of objective
is increased, descent directions are found only for two randomly chosen objectives. Furthermore,
in order to increase the search pressure in high-dimensional objective space, we
impose an additional condition for the acceptance of descent directions found for leaders
during local search. The performance of the proposed approach is compared with those produced
by representative state-of-the-art multiobjective evolutionary algorithms on a set of
problems with up to 8 objectives. The experimental results reveal that our algorithm is able
to produce highly competitive results with well-established multiobjective optimizers on all
tested problems.Moreover, due to its hybrid reproduction operator, DDMOA2 demonstrates
superior performance on multimodal problems.This work has been supported by FCT Fundação para a Ciência e Tecnologia in the
scope of the project: PEst-OE/EEI/UI0319/2014
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
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
Evolutionary multiobjective optimization : review, algorithms, and applications
Programa Doutoral em Engenharia Industrial e SistemasMany mathematical problems arising from diverse elds of human activity can be formulated
as optimization problems. The majority of real-world optimization problems involve
several and con
icting objectives. Such problems are called multiobjective optimization
problems (MOPs). The presence of multiple con
icting objectives that have to be simultaneously
optimized gives rise to a set of trade-o solutions, known as the Pareto optimal
set. Since this set of solutions is crucial for e ective decision-making, which generally aims
to improve the human condition, the availability of e cient optimization methods becomes
indispensable.
Recently, evolutionary algorithms (EAs) have become popular and successful in approximating
the Pareto set. The population-based nature is the main feature that makes them
especially attractive for dealing with MOPs. Due to the presence of two search spaces,
operators able to e ciently perform the search in both the decision and objective spaces
are required. Despite the wide variety of existing methods, a lot of open research issues in
the design of multiobjective evolutionary algorithms (MOEAs) remains.
This thesis investigates the use of evolutionary algorithms for solving multiobjective
optimization problems. Innovative algorithms are developed studying new techniques for
performing the search either in the decision or the objective space. Concerning the search
in the decision space, the focus is on the combinations of traditional and evolutionary
optimization methods. An issue related to the search in the objective space is studied in
the context of many-objective optimization.
Application of evolutionary algorithms is addressed solving two di erent real-world
problems, which are modeled using multiobjective approaches. The problems arise from
the mathematical modelling of the dengue disease transmission and a wastewater treatment
plant design. The obtained results clearly show that multiobjective modelling is an e ective
approach. The success in solving these challenging optimization problems highlights the
practical relevance and robustness of the developed algorithms.Muitos problemas matemáticos que surgem nas diversas áreas da atividade humana podem ser formulados como problemas de otimização. A maioria dos problemas do mundo real envolve vários objetivos conflituosos. Tais problemas chamam-se problemas de otimização multiobjetivo. A presença de vários objetivos conflituosos, que têm de ser otimizados em simultâneo, dá origem a um conjunto de soluções de compromisso, conhecido como conjunto de soluções ótimas de Pareto. Uma vez que este conjunto de soluções é fundamental para uma tomada de decisão eficaz, cujo objetivo em geral é melhorar a condição humana, o desenvolvimento de métodos de otimização eficientes torna-se indispensável.
Recentemente, os algoritmos evolucionários tornaram-se populares e bem-sucedidos na aproximação do conjunto de Pareto. A natureza populacional é a principal característica que os torna especialmente atraentes para lidar com problemas de otimização multiobjetivo.
Devido à presença de dois espaços de procura, operadores capazes de realizar a procura de forma eficiente, tanto no espaço de decisão como no espaço dos objetivos, são necessários. Apesar da grande variedade de métodos existentes, várias questões de investigação permanecem em aberto na área do desenvolvimento de algoritmos evolucionários multiobjetivo.
Esta tese investiga o uso de algoritmos evolucionários para a resolução de problemas de otimização multiobjetivo. São desenvolvidos algoritmos inovadores que estudam novas técnicas de procura, quer no espaço de decisão, quer no espaço dos objetivos. No que diz respeito à procura no espaço de decisão, o foco está na combinação de métodos de otimização tradicionais com algoritmos evolucionários. A questão relacionada com a procura no espaço dos objetivos é desenvolvida no contexto da otimização com muitos objetivos.
A aplicação dos algoritmos evolucionários é abordada resolvendo dois problemas reais, que são modelados utilizando abordagens multiobjectivo. Os problemas resultam da modelação matemática da transmissão da doença do dengue e do desenho ótimo de estações de tratamento de águas residuais. O sucesso na resolução destes problemas de otimização constitui um desafio e destaca a relevância prática e robustez dos algoritmos desenvolvidos
Optimization. An attempt at describing the State of the Art
This paper is an attempt at describing the State of the Art of the vast field of continuous optimization. We will survey deterministic and stochastic methods as well as hybrid approaches in their application to single objective and multiobjective optimization. We study the parameters of optimization algorithms and possibilities for tuning them. Finally, we discuss several methods for using approximate models for computationally expensive problems
A Gradient Multiobjective Particle Swarm Optimization
An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (MOG) method, is developed to improve the computation performance. In this AGMOPSO algorithm, the MOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Attributed to the MOG method, this AGMOPSO algorithm not only has faster convergence speed and higher accuracy but also its solutions have better diversity. Additionally, the convergence is discussed to confirm the prerequisite of any successful application of AGMOPSO. Finally, with regard to the computation performance, the proposed AGMOPSO algorithm is compared with some other multiobjective particle swarm optimization (MOPSO) algorithms and two state-of-the-art multiobjective algorithms. The results demonstrate that the proposed AGMOPSO algorithm can find better spread of solutions and have faster convergence to the true Pareto-optimal front
IMRT beam angle optimization using electromagnetism-like algorithm
The selection of appropriate beam irradiation directions in radiotherapy – beam angle optimization (BAO) problem – is very impor- tant for the quality of the treatment, both for improving tumor irradia- tion and for better organs sparing. However, the BAO problem is still not solved satisfactorily and, most of the time, beam directions continue to be manually selected in clinical practice which requires many trial and error iterations between selecting beam angles and computing fluence patterns until a suitable treatment is achieved. The objective of this pa- per is to introduce a new approach for the resolution of the BAO problem, using an hybrid electromagnetism-like algorithm with descent search to tackle this highly non-convex optimization problem. Electromagnetism- like algorithms are derivative-free optimization methods with the ability to avoid local entrapment. Moreover, the hybrid electromagnetism-like algorithm with descent search has a high ability of producing descent directions. A set of retrospective treated cases of head-and-neck tumors at the Portuguese Institute of Oncology of Coimbra is used to discuss the benefits of the proposed algorithm for the optimization of the BAO problem.Fundação para a Ciência e a Tecnologia (FCT
Evolutionary Reinforcement Learning: A Survey
Reinforcement learning (RL) is a machine learning approach that trains agents
to maximize cumulative rewards through interactions with environments. The
integration of RL with deep learning has recently resulted in impressive
achievements in a wide range of challenging tasks, including board games,
arcade games, and robot control. Despite these successes, there remain several
crucial challenges, including brittle convergence properties caused by
sensitive hyperparameters, difficulties in temporal credit assignment with long
time horizons and sparse rewards, a lack of diverse exploration, especially in
continuous search space scenarios, difficulties in credit assignment in
multi-agent reinforcement learning, and conflicting objectives for rewards.
Evolutionary computation (EC), which maintains a population of learning agents,
has demonstrated promising performance in addressing these limitations. This
article presents a comprehensive survey of state-of-the-art methods for
integrating EC into RL, referred to as evolutionary reinforcement learning
(EvoRL). We categorize EvoRL methods according to key research fields in RL,
including hyperparameter optimization, policy search, exploration, reward
shaping, meta-RL, and multi-objective RL. We then discuss future research
directions in terms of efficient methods, benchmarks, and scalable platforms.
This survey serves as a resource for researchers and practitioners interested
in the field of EvoRL, highlighting the important challenges and opportunities
for future research. With the help of this survey, researchers and
practitioners can develop more efficient methods and tailored benchmarks for
EvoRL, further advancing this promising cross-disciplinary research field
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A solution to the crucial problem of population degeneration in high-dimensional evolutionary optimization
Three popular evolutionary optimization algorithms are tested on high-dimensional benchmark functions. An important phenomenon responsible for many failures - population degeneration - is discovered. That is, through evolution, the population of searching particles degenerates into a subspace of the search space, and the global optimum is exclusive from the subspace. Subsequently, the search will tend to be confined to this subspace and eventually miss the global optimum. Principal components analysis (PCA) is introduced to discover population degeneration and to remedy its adverse effects. The experiment results reveal that an algorithm's efficacy and efficiency are closely related to the population degeneration phenomenon. Guidelines for improving evolutionary algorithms for high-dimensional global optimization are addressed. An application to highly nonlinear hydrological models demonstrates the efficacy of improved evolutionary algorithms in solving complex practical problems. © 2011 IEEE
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