Recently, surrogate-assisted evolutionary algorithms (SAEAs) received a lot of attention due to their excellent performance in handling expensive constrained optimization problems (ECOPs). However, most of them can on...
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Recently, surrogate-assisted evolutionary algorithms (SAEAs) received a lot of attention due to their excellent performance in handling expensive constrained optimization problems (ECOPs). However, most of them can only be used for solving problems that are low-dimensional and with only continuous variables. Therefore, a surrogate-assisted differential evolution for high-dimensional ECOPs with mixed-integer variables (SADE-HDMI) is proposed in this paper. Firstly, a Multiple Local Extremum based Sampling (MLES) method is designed, in which two sampling strategies focusing on constraints and objective functions are utilized alternatively based on iterative information, so that the feasible region and high-quality feasible solutions can be efficiently located. Secondly, a Diverse Population Generation Operation for Mixed-Integer Variables (DPMI) is devised to avoid the population from falling into a local optimal region, where the diversity of the population is maintained by selecting solutions with more diversity and limiting the number of solutions with the same integer variables in the population. Convergence and diversity can be well balanced under the help of these two operations. Finally, the performance of SADE-HDMI is validated on fifteen benchmarks and a real-world optimization problem. The optimization results demonstrate that SADE-HDMI can locate feasible solutions with 100% probability on these 16 problems, and it is superior to or similar to other three state-of-the-art algorithms on 15 out of 16 problems.
This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained optimization problems where two surrogate optimization phases are executed ...
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This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained optimization problems where two surrogate optimization phases are executed collaboratively at each generation. As the complexity of optimizationproblems and the cost of solutions increase in practical applications, how to efficiently solve expensive constrained optimization problems with limited computational resources has become an important area of research. Traditional optimization algorithms often struggle to balance the efficiency of global and local searches, especially when dealing with high-dimensional and complex constraint conditions. For global surrogate-assisted collaborative evolution phase, the global candidate set is generated through classification collaborative mutation operations to alleviate the pre-screening pressure of the surrogate model. For local surrogate-assisted phase, a distributed central region local exploration is designed to achieve intensively search for promising distributed local areas which are located by affinity propagation clustering and mathematical modeling. More importantly, a three-layer adaptive selection strategy where the feasibility, diversity and convergence are balanced effectively is designed to identify promising solutions in global and local candidate sets. Therefore, the SGDLCO efficiently balances global and local search during the whole optimization process. Experimental studies on five classical test suites demonstrate that the SGDLCO provides excellent performance in solving expensive constrained optimization problems.
expensive constrained optimization problems (ECOPs) widely exist in various scientific and industrial applications. Surrogate-Assisted Evolutionary Algorithms (SAEAs) have recently exhibited great ability in solving t...
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expensive constrained optimization problems (ECOPs) widely exist in various scientific and industrial applications. Surrogate-Assisted Evolutionary Algorithms (SAEAs) have recently exhibited great ability in solving these expensiveoptimizationproblems. This paper proposes a Surrogate-Assisted Classification-Collaboration Differential Evolution (SACCDE) algorithm for ECOPs with inequality constraints. In SACCDE, the current population is classified into two subpopulations based on certain feasibility rules, and a classification-collaboration mutation operation is designed to generate multiple promising mutant solutions by not only using promising information in good solutions but also fully exploiting potential information hidden in bad solutions. Afterwards, the surrogate is utilized to identify the most promising offspring solution for accelerating the convergence speed. Furthermore, considering that the population diversity may decrease due to the excessive incorporation of greedy information brought by the classified solutions, a global search framework that can adaptively adjust the classification-collaboration mutation operation based on the iterative information is introduced for achieving an effective global search. Therefore, the proposed algorithm can strike a well balance between local and global search. The experimental results of SACCDE and other state-of-the-art algorithms demonstrate that the performance of SACCDE is highly competitive. (C) 2019 Elsevier Inc. All rights reserved.
Surrogates have recently shown excellent abilities in assisting evolutionary algorithms for solving computationally expensive constrained optimization problems (ECOPs). However, the effectiveness of such surrogate-ass...
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ISBN:
(纸本)9781728121536
Surrogates have recently shown excellent abilities in assisting evolutionary algorithms for solving computationally expensive constrained optimization problems (ECOPs). However, the effectiveness of such surrogate-assisted evolutionary algorithms has only been verified on ECOPs with inequality constraints. In this paper, a Novel Surrogate-Assisted Differential Evolution (NSADE) algorithm is proposed for solving ECOPs with equality and inequality constraints, in which a trial vector generation mechanism and two surrogate-assisted local search phases are carried out iteratively. The trial vector generation mechanism based on information exchange between the historical elite solution set and current population is utilized to balance exploiting potential areas and exploring unknown areas. Then the expectation improvement-based local search is used to not only guide the current population to move towards feasible region but also alleviate the inaccuracy of the surrogate on the constraint boundary. Finally, a solution identification-based local search is utilized to further optimize two different types of historical elite solutions. Empirical studies on fifteen widely used benchmark problems demonstrate that the proposed NSADE can effectively obtain high-quality feasible solutions on ECOPs with equality constraints under a limited computational budget.
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