On the hybridization of global and local search methods

Khanum, Rashida Adeeb; Jan, Muhammad Asif; Khan, Wali Ullah; Tairan, Nasser; Khan, Hidayat Ullah; Shah, Habib

doi:10.3233/jifs-17657

Cited by 12 publications

(10 citation statements)

References 30 publications

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“…Two LS strategies, Trigonometric and Interpolated, are inserted in DE to enhance its poor exploration. Two other LS techniques are merged in DE along with a restart strategy to improve its global exploration [54]. This algorithm is statistically sound, as the obtained results are better than other algorithms.…”

Section: Related Workmentioning

confidence: 99%

Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization

et al. 2019

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Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon–Fletcher–Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the archive and refined there by DFP. Thus, both kinds of searches, global and local, are incorporated alternatively. Furthermore, a population minimization approach is also proposed. At each call of DFP, the population is decreased. The algorithm starts with a maximum population and ends up with a minimum. The proposed technique was tested on a test suite of 28 complex functions selected from literature to evaluate its merit. The results achieved demonstrate that DE complemented with LS can further enhance the performance of RJADE/TA.

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Section: Related Workmentioning

confidence: 99%

Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization

et al. 2019

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“…In the last two decades and so, hybrid evolutionary algorithms(EAs) have got much attention for dealing with optimization problems with high complexity, noisy environment, imprecision, uncertainty and vagueness [1,6,42,37,38,39,40,4,5,61,3,41,26,45,46].In this paper, We have developed an efficient constrained hybrid constrained EAs by incorporating some existing penalty functions with static and self-adaptive procedures. The suggested HCEAs employs Differential evolution (DE) [52] and Particle Swarm Optimization(PSO) [25] as constituent search operators to perform their search process.…”

Section: Hybrid Constrained Evolutionary Algorithmmentioning

confidence: 99%

Constrained Optimization with Hybrid Evolutionary Algorithms

Khan¹,

Yeniay²,

Shah³

et al. 2018

HJMS

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Constrained optimization are naturally arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers.Evolutionary algorithms are not directly applied on constrained optimization problems. However, different constraint-handling techniques are incorporated in their framework to adopt it for dealing with constrained environments. This paper suggests an hybrid constrained evolutionary algorithm (HCEA) that employs two penalty functions simultaneously. The suggested HCEA has two versions namely HCEA-static and HCEA-adaptive. The performance of the HCEA-static and HCEA-adaptive algorithms are examined upon the constrained benchmark functions that are recently designed for the special session of the 2006 IEEE Conference of Evolutionary Computation (IEEE-CEC'06). The experimental results of the suggested algorithms are much promising as compared to one of the recent constrained version of the JADE. The converging behaviour of the both suggested algorithms on each benchmark function is encouraging and promising in most cases.

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“…Recently, many ensemble strategies were proposed, which benefit from both the availability of diverse approaches and adaptive tuning of associated intrinsic parameters [51]. Many researches have shown the general applicability of the ensemble strategy in solving diverse problems by using different populated optimization algorithms [29,52,53]. e performance of the suggested MSIA was evaluated over recently designed benchmark functions for the special 2 Complexity session of evolutionary algorithms competition in 2013-IEEE-Congress on Evolutionary Computing (IEEE-CEC′13) [54].…”

Section: Introductionmentioning

confidence: 99%

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

2021

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Constrained optimization plays an important role in many decision-making problems and various real-world applications. In the last two decades, various evolutionary algorithms (EAs) were developed and still are developing under the umbrella of evolutionary computation. In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. Particle swarm optimization (PSO), firefly algorithm, ant colony optimization (ACO), and bat algorithm (BA) have gained much popularity and they have successfully tackled various test suites of benchmark functions and real-world problems. These SI-based algorithms follow the social and interactive principles to perform their search process while approximating solution for the given problems. In this paper, a multiswarm-intelligence-based algorithm (MSIA) is developed to cope with bound constrained functions. The suggested algorithm integrates the SI-based algorithms to evolve population and handle exploration versus exploitation issues. Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. The test suite of benchmark function is recently designed for the special session of EAs competition in IEEE Congress on Evolutionary Computation (IEEE-CEC′13). The suggested algorithm has approximated promising solutions with good convergence and diversity maintenance for most of the used bound constrained single optimization problems.

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On the hybridization of global and local search methods

Cited by 12 publications

References 30 publications

Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization

Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization

Constrained Optimization with Hybrid Evolutionary Algorithms

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems

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