A Survey on Metaheuristics for Solving Large Scale Optimization Problems

Singh, Atinesh; Jana, Nanda Dulal

doi:10.5120/ijca2017914839

Cited by 9 publications

(7 citation statements)

References 36 publications

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“…High dimensional optimization has two main issues one is that within the increase of dimensions number, the number of possible solutions is grown extensively [1,3]. And the other is that the search space extended exponentially [1,3]. These two issues make the algorithm face a difficulty to achieve an optimal solution at the appropriate time [11].…”

Section: High Dimensional Optimization Problemmentioning

confidence: 99%

“…Computing global optimal for the high dimensional optimization problem is complex [2], and considers being one of the most challenges facing the optimization algorithms. Since optimization problems appear in diverse fields including engineering, manufacture and the economic system, there is a necessary need for an efficient algorithm that could solve the high dimensional problem successfully [3].…”

Section: Introductionmentioning

confidence: 99%

“…Two main types of global optimization methods are founded in literature: deterministic and probabilistic [4]. Deterministic algorithms deal with the problem by making a clear assumption of it and exploring the search space in a suitable way to achieve a fixed solution within an acceptable amount of time [4]. These algorithms could not use in such problems that have large search area such as high dimensional optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem

Bashath¹,

Ismail²

2018

IJEECS

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<p>High dimensional optimization considers being one of the most challenges that face the algorithms for finding an optimal solution for real-world problems. These problems have been appeared in diverse practical fields including business and industries. Within a huge number of algorithms, selecting one algorithm among others for solving the high dimensional optimization problem is not an easily accomplished task. This paper presents a comprehensive study of two swarm intelligence based algorithms: 1-particle swarm optimization (PSO), 2-cuckoo search (CS).The two algorithms are analyzed and compared for problems consisting of high dimensions in respect of solution accuracy, and runtime performance by various classes of benchmark functions.</p><p> </p>

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Section: High Dimensional Optimization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem

Bashath¹,

Ismail²

2018

IJEECS

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“…The algorithms for LSGO problems can be roughly classified into three categories: standard evolutionary algorithms, CC-based evolutionary algorithms, and memetic algorithms [ 22 ]. The memetic algorithm (MA) [ 23 ] is a combination of global search and local search.…”

Section: Introductionmentioning

confidence: 99%

A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization

Zhang

Lan

2022

Computational Intelligence and Neuroscience

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In many fields, including management, computer, and communication, Large-Scale Global Optimization (LSGO) plays a critical role. It has been applied to various applications and domains. At the same time, it is one of the most challenging optimization problems. This paper proposes a novel memetic algorithm (called MPCE & SSALS) based on multiparent evolution and adaptive local search to address the LSGO problems. In MPCE & SSALS, a multiparent crossover operation is used for global exploration, while a step-size adaptive local search is utilized for local exploitation. A new offspring is generated by recombining four parents. In the early stage of the algorithm execution, global search and local search are performed alternately, and the population size gradually decreases to 1. In the later stage, only local searches are performed for the last individual. Experiments were conducted on 15 benchmark functions of the CEC′2013 benchmark suite for LSGO. The results were compared with four state-of-the-art algorithms, demonstrating that the proposed MPCE & SSALS algorithm is more effective.

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“…As a result, researchers have proposed various algorithms and techniques to overcome these problems. These studies can be roughly divided into three classes; standard evolutionary algorithms, cooperative coevolution (CC)-based evolutionary algorithms and memetic algorithms (Singh and Jana 2017). The proposed method in this paper is a novel local search method which can be classified as a memetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

A novel local search method for LSGO with golden ratio and dynamic search step

Koçer

Uymaz

2020

Soft Comput

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Depending on the developing technology, large-scale problems have emerged in many areas such as business, science, and engineering. Therefore, large-scale optimization problems and solution techniques have become an important research field. One of the most effective methods used in this research field is memetic algorithm which is the combination of evolutionary algorithms and local search methods. The local search method is an important part that greatly affects the memetic algorithm's performance. In this paper, a novel local search method which can be used in memetic algorithms is proposed. This local search method is named as golden ratio guided local search with dynamic step size (GRGLS). To evaluate the performance of proposed local search method, two different performance evaluations were performed. In the first evaluation, memetic success history-based adaptive differential evolution with linear population size reduction and semi-parameter adaptation (MLSHADE-SPA) was chosen as the main framework and comparison is made between three local search methods which are GRGLS, multiple trajectory search local search (MTS-LS1) and modified multiple trajectory search. In the second evaluation, the improved MLSHADE-SPA (IMLSHADE-SPA) framework which is a combination of MLSHADE-SPA framework and proposed local search method (GRGLS) was compared with some recently proposed nine algorithms. Both of the experiments were performed using CEC'2013 benchmark set designed for large-scale global optimization. In general terms, the proposed method achieves good results in all functions, but it performs superior on overlapping and non-separable functions.Keywords Large-scale global optimization Á Local search Á Golden ratio Á Memetic algorithm Á CEC'2013 LSGO benchmark Communicated by V. Loia.

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A Survey on Metaheuristics for Solving Large Scale Optimization Problems

Cited by 9 publications

References 36 publications

Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem

Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem

A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization

A novel local search method for LSGO with golden ratio and dynamic search step

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