Deterministic oscillatory search: a new meta-heuristic optimization algorithm

Archana, N.; Vidhyapriya, R.; Benedict, Antony; Chandran, Karthik

doi:10.1007/s12046-017-0635-7

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…( 2005 ) 158 Deterministic Oscillatory Search (DOS) Archana et al. ( 2017 ) 159 Dialectic Search (DS) Kadioglu and Sellmann ( 2009 ) 160 Differential Evolution (DE) Storn and Price ( 1997 ) 161 Differential Search Algorithm (DSA) Civicioglu ( 2012 ) 162 Dolphin Echolocation (DE) Kaveh and Farhoudi ( 2013 ) 163 Dolphin Partner Optimization (DPO) Shiqin et al. ( 2009 ) 164 Dragonfly Algorithm (DA) Mirjalili ( 2016a ) 165 Driving Training-Based Optimization (DTBO) Dehghani 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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“…If the movement produces better results, the particle continues to find another local optimal solution and this process continues until the maximum number of evaluations is reached. The procedure to apply DOS algorithm to the chemotherapy optimization problem under study is briefed in the following pseudo code [29]:…”

Section: ……………… (11)mentioning

confidence: 99%

“…Distributed evolutionary computing [22], adaptive neural network [23], genetic algorithm [24], strength Pareto evolutionary algorithm [25], particle swarm optimization [26] and various hybrid approaches [27,28] have been successfully applied to find an optimal drug regimen to treat cancer patients. In this paper, a new optimization algorithm, Deterministic Oscillatory Search (DOS) [29] has been applied to solve the Gompertzian chemotherapy drug regimen problem with an aim to reduce the number of tumor cells and maintain the toxicity levels within limits.…”

Section: Introductionmentioning

confidence: 99%

Chemotherapy drug regimen optimization using deterministic oscillatory search algorithm

Archana¹,

Benedict²,

J³

2018

Trop. J. Pharm Res

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Purpose: To schedule chemotherapy drug delivery using Deterministic Oscillatory Search algorithm, keeping the toxicity level within permissible limits and reducing the number of tumor cells within a predefined time period. Methods: A novel metaheuristic algorithm, deterministic oscillatory search, has been used to optimize the Gompertzian model of the drug regimen problem. The model is tested with fixed (fixed interval variable dose, FIVD) and variable (variable interval variable dose, VIVD) interval schemes and the dosage presented for 52 weeks. In the fixed interval, the treatment plan is fixed in such a way that doses are given on the first two days of every seven weeks such as day 7, day 14, etc. Results: On comparing the two schemes, FIVD provided a higher reduction in the number of tumor cells by 98 % compared to 87 % by VIVD after the treatment period. Also, a significant reduction in the number was obtained half way through the regimen. The dose level and toxicity are also reduced in the FIVD scheme. The value of drug concentration is more in FIVD scheme (50) compared to VIVD (41); however, it is well within the acceptable limits of concentration. The results proved the effectiveness of the proposed technique in terms of reduced drug concentration, toxicity, tumor size and drug level within a predetermined time period. Conclusion: Artificial intelligent techniques can be used as a tool to aid oncologists in the effective treatment of cancer through chemotherapy.

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Maximization of System Load-Ability and Voltage Stability Index Using Distribution Based Salp Swarm Optimization

Dash

Subhashini

2022

Electric Power Components and Systems

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Deterministic oscillatory search: a new meta-heuristic optimization algorithm

Cited by 6 publications

References 24 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

Chemotherapy drug regimen optimization using deterministic oscillatory search algorithm

Maximization of System Load-Ability and Voltage Stability Index Using Distribution Based Salp Swarm Optimization

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