Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big ...data also make optimization problems with more challenges including
M
any-dimensions,
M
any-changes,
M
any-optima,
M
any-constraints, and
M
any-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including
reducing problem difficulty
,
increasing algorithm diversity
,
accelerating convergence speed
,
reducing running time
, and
extending application field
. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.
In many-objective optimization problems (MaOPs), forming sound tradeoffs between convergence and diversity for the environmental selection of evolutionary algorithms is a laborious task. In ...particular, strengthening the selection pressure of population toward the Pareto-optimal front becomes more challenging, since the proportion of nondominated solutions in the population scales up sharply with the increase of the number of objectives. To address these issues, this paper first defines the nondominated solutions exhibiting evident tendencies toward the Pareto-optimal front as prominent solutions, using the hyperplane formed by their neighboring solutions, to further distinguish among nondominated solutions. Then, a novel environmental selection strategy is proposed with two criteria in mind: 1) if the number of nondominated solutions is larger than the population size, all the prominent solutions are first identified to strengthen the selection pressure. Subsequently, a part of the other nondominated solutions are selected to balance convergence and diversity and 2) otherwise, all the nondominated solutions are selected; then a part of the dominated solutions are selected according to the predefined reference vectors. Moreover, based on the definition of prominent solutions and the new selection strategy, we propose a hyperplane assisted evolutionary algorithm, referred here as hpaEA , for solving MaOPs. To demonstrate the performance of hpaEA , extensive experiments are conducted to compare it with five state-of-the-art many-objective evolutionary algorithms on 36 many-objective benchmark instances. The experimental results show the superiority of hpaEA which significantly outperforms the compared algorithms on 20 out of 36 benchmark instances.
Interactive evolutionary computation (IEC) has demonstrated significant success in addressing numerous real-world problems that are challenging to quantify mathematically or are inadequately ...evaluated using conventional computational models. This success arises from IEC’s ability to effectively amalgamate evolutionary computation (EC) algorithms with expert knowledge and user preferences. These problems encompass the creative and personalized generation of products, art, and sound; the design optimization of communication systems, environments, and pharmaceuticals; and expert support in areas such as portfolio selection and hearing aid fitting, among others. Despite significant advancements in IEC over the past two decades, no major comprehensive survey encompassing all aspects of IEC research has been conducted since 2001. This article aims to address this gap by providing a comprehensive survey and an enriched definition and scope of IEC, along with innovative ideas for future research in this field. The proposed IEC definition more clearly reflects the mechanism and current research status of the IEC. Additionally, the survey categorizes IEC research into five distinct directions from a problem-oriented perspective: interactive evolutionary computation algorithms, IEC algorithm improvements, evolutionary multi-objective optimization (EMO) with IEC, human perception studies with IEC, and IEC applications. Each direction is meticulously explored, elucidating its contents and key features, while providing a concise summary of pertinent IEC studies. Finally, the survey investigates several promising future trends in IEC, analyzing them through the lens of these five directions and considering the current perspective of computational intelligence, artificial intelligence, and human-machine interaction.
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•A comprehensive survey of interactive evolutionary computation over the last two decades.•A new interactive evolutionary computation definition.•Five existing interactive evolutionary computation development directions.•Several promising future trends of interactive evolutionary computation in the field of artificial intelligence.
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