Improving DE-based cooperative coevolution for constrained large-scale global optimization problems using an increasing grouping strategy

Vakhnin, Aleksei; Sopov, Evgenii

doi:10.1088/1757-899x/734/1/012099

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…In addition to our previous study [17], this paper was extended by new numerical experiments. The performance of the ε-iCC-SHADE algorithm has been investigated with different mutation strategies and different population sizes on the scaled problems from the IEEE CEC 2017 Competition on constrained real-parameter optimization.…”

Section: Introductionmentioning

confidence: 97%

Investigation of the iCC Framework Performance for Solving Constrained LSGO Problems

Vakhnin

Sopov

2020

Algorithms

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Many modern real-valued optimization tasks use “black-box” (BB) models for evaluating objective functions and they are high-dimensional and constrained. Using common classifications, we can identify them as constrained large-scale global optimization (cLSGO) tasks. Today, the IEEE Congress of Evolutionary Computation provides a special session and several benchmarks for LSGO. At the same time, cLSGO problems are not well studied yet. The majority of modern optimization techniques demonstrate insufficient performance when confronted with cLSGO tasks. The effectiveness of evolution algorithms (EAs) in solving constrained low-dimensional optimization problems has been proven in many scientific papers and studies. Moreover, the cooperative coevolution (CC) framework has been successfully applied for EA used to solve LSGO problems. In this paper, a new approach for solving cLSGO has been proposed. This approach is based on CC and a method that increases the size of groups of variables at the decomposition stage (iCC) when solving cLSGO tasks. A new algorithm has been proposed, which combined the success-history based parameter adaptation for differential evolution (SHADE) optimizer, iCC, and the ε-constrained method (namely ε-iCC-SHADE). We investigated the performance of the ε-iCC-SHADE and compared it with the previously proposed ε-CC-SHADE algorithm on scalable problems from the IEEE CEC 2017 Competition on constrained real-parameter optimization.

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Section: Introductionmentioning

confidence: 97%

Investigation of the iCC Framework Performance for Solving Constrained LSGO Problems

Vakhnin

Sopov

2020

Algorithms

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“…It is a "divide and conquer" strategy initially proposed by Potter and De Jong in [25]. In the literature, the cooperative coevolutionary approach has been successfully applied for various optimization algorithms such as GA [26], PSO [27], DE [28], Simulated Annealing (SA) [29], Ant Colony Optimization (ACO) [30,31], Firefly algorithm [32], and many others. On the other hand, a large quantity of evaluation, due to the large number of problem decision variables, also implies an increased prohibitive computation time.…”

Section: Introductionmentioning

confidence: 99%

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

Jarray

Al‐Dhaifallah

Rezk

2022

Sensors

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The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time.

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Using the iCC framework for solving unconstrained LSGO problems

Vakhnin

Sopov

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Evolutionary computation is a branch of computational intelligence that is inspired by nature. This algorithm family uses fundamental rules of natural selection to find a better solution when solving optimization problems. The productivity of these methods decreases when the number of parameters to be optimized is extra-large. This area of research is known as large-scale global optimization. Cooperative coevolution is a structure for evolutionary algorithms which tries to solve the curse of dimensionality problem. The cooperative coevolution efficiency of application largely depends on the two settings: the size of each subcomponent(subproblem) and the grouping of variables. The actual work proposes improved cooperative coevolution denoted as iCC. The iCC approach dynamically resizes the groups. iCC starts with a predefined set of subproblems and reduces them gradually during the optimization process. A novel metaheuristic has been developed which is called iCC-SHADE for black-box optimization problems with a large number of variables. The proposed method has been tested on fifteen optimization tasks from the LSGO CEC’2013 competition benchmark. The experimental results have demonstrated that iCC-SHADE has statistically better performance than CC-SHADE with a static number of subproblems. Also, the effectiveness of iCC-SHADE has been tested in comparison with other modern metaheuristics. The Wilcoxon rank-sum test was used to compare the effectiveness of investigated metaheuristics.

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Improving DE-based cooperative coevolution for constrained large-scale global optimization problems using an increasing grouping strategy

Cited by 4 publications

References 17 publications

Investigation of the iCC Framework Performance for Solving Constrained LSGO Problems

Investigation of the iCC Framework Performance for Solving Constrained LSGO Problems

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

Using the iCC framework for solving unconstrained LSGO problems

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