Search in forest optimizer: a bioinspired metaheuristic algorithm for global optimization problems

Ahwazian, Amin; Amindoust, Atefeh; Tavakkoli‐Moghaddam, Reza; Nikbakht, Mehrdad

doi:10.1007/s00500-021-06522-6

Cited by 7 publications

(4 citation statements)

References 72 publications

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“…( 2015 ) 411 Search in Forest Optimizer (SFO) Ahwazian et al. ( 2022 ) 412 Seed based Plant Propagation Algorithm (SPPA) Sulaiman and Salhi ( 2015 ) 413 Seeker Optimization Algorithm (SOA) Dai et al. ( 2006 ) 414 See-See Partidge Chicks Optimization (SSPCO) Omidvar et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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“…-The proposed global optimizer algorithm was compared to other metaheuristic algorithms. Search in forest optimizer Ahwazian et al [70] 2022 -Four well-known standardized examinations, including traditional unimodal and multimodal functions, CEC2014 unimodal and multimodal functions, and CEC2014 combined functions. Tasmanian Devil Optimization Dehghani et al [71] 2022 -Twenty-three standard objective functions are used to evaluate the efficacy of DO in optimization.…”

Section: Mohammadmentioning

confidence: 99%

“…The algorithm is based on the systematic behavior of search teams searching a forest for missing individuals [70]. According to the SIFO optimizer, a number of teams comprised of numerous professionals in the search field are dispersed across the forest and progressively advance in the same direction by discovering evidence from the target until the missing person is located.…”

Section: Search In Forest Optimizer (Sifo)mentioning

confidence: 99%

Metaheuristics-based maximum power point tracking for PV systems: a review

Jamhari,

Hashim,

Othman

et al. 2023

IJPEDS

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<span lang="EN-US">Over the years, numerous maximum power point tracking (MPPT) methods have been developed to extract the maximum available power from PV arrays. They are generally categorized as conventional or metaheuristic methods. The most employed conventional methods include perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC), due to their simplicity and ease of implementation. However, under partial shading condition (PSC), none of them can effectively locate a global maximum power point (GMPP) out of many local maximum power points (LMPPs). This results in significant power loss during PSC, prompting the development of various metaheuristic-based MPPT methods to address the problem. This paper reviews 38 existing metaheuristic-based MPPTs and 27 metaheuristic methods that have not yet been applied to any MPPT operation up to date. Metaphorically, these methods are divided into four categories: (i) evolutionary-based, (ii) physics-based, (iii) swarm-based, and (iv) human-based. The different MPPTs are compared in terms of complexity, converter topology, and PSC tracking capability. This paper is intended to serve as a one-stop resource for any researcher, practitioner, or advanced student seeking to develop a new metaheuristic-based MPPT method.</span>

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“…SIFO can easily surpass local optimums to conduct its search simultaneously in the main path alongside all other possible paths. It can also exit its local optimum to find the best optimum response; therefore, SIFO can present a more effective performance in linear planning model solving than other metaheuristic algorithms (Ahwazian et al, 2022). Now that the model is verified, it will be solved for oil-producing countries of OPEC.…”

Section: Proposed Model's Formulationmentioning

confidence: 99%

A mathematical tri-level programming model for designing an integrated dynamic petroleum product supply chain

Ahwazian

Amindoust

Tavakkoli–Moghaddam

et al. 2022

JAMR

Self Cite

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PurposeThe purpose of this paper is to design petroleum products’ supply chain management, which includes efficient integration of suppliers, manufacturers, storehouses and retailers.Design/methodology/approachThis paper proposes that a three-level supply chain will be turned into a bi-level supply chain of petroleum products by simultaneous integration of the middle level with the upstream and downstream levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels. Also, it is integrally optimized by considering the multiple managerial flows' mutual results at various supply chain levels.FindingsThe concepts of the design, structure and outputs are led by the model's solution. The model also responds to the variations in the market via coordination in the related decisions to the distribution, production and inventory issues, and also coordinating between the demands and production.Research limitations/implicationsThis paper has limited its analysis to definite values due to the over-expansion of calculations and analysis. Future works can study other aspects of the proposed model for a multi-level petroleum product supply chain in different states of certain parameters and time zones.Practical implicationsThe designed model can directly and transparently help the oil managers and decision-makers lower the costs of manufacturing, distribution and sales with respect to the determined criteria.Originality/valueThis paper establishes that effectiveness of the dynamic petroleum materials supply chain design will increase by considering maintained and increased production costs and coordinate management flows at all levels by supply chain creation’s integration.

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Search in forest optimizer: a bioinspired metaheuristic algorithm for global optimization problems

Cited by 7 publications

References 72 publications

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Metaheuristics-based maximum power point tracking for PV systems: a review

A mathematical tri-level programming model for designing an integrated dynamic petroleum product supply chain

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