Improved symbiotic organisms search algorithm for solving unconstrained function optimization

Nama, Sukanta; Saha, Apu Kumar; Ghosh, Sima

doi:10.5267/j.dsl.2016.2.004

Cited by 46 publications

(19 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [10,11], SOS has been applied to solve the optimal power flow and the economic emission load dispatch problems. Moreover, SOS has been successfully applied in the optimal design of linear and circular antenna arrays [12,13], in solving the load frequency control problem [14], in structure optimization problems [15], in solving unconstrained function optimization [16], in the design of an improved 3D Turbo Code [17], in the optimal design of analog active filters [18], in the synthesis of elliptical antenna arrays [19], and other engineering areas [20][21][22][23][24]. The results indicated that SOS gives very good results and is very competitive with the state of the art for the solution of these problems, which motivated this work.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

Dib¹,

Al-Sammarraie²

2020

IJECE

View full text Add to dashboard Cite

This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

Dib¹,

Al-Sammarraie²

2020

IJECE

View full text Add to dashboard Cite

show abstract

“…The first attempt to start these types of studies is genetic algorithm (GA) (Holland, 1992) which actually employs the natural process of genetic evolution. After that various nature-inspired meta-heuristic approaches have been proposed such as differential evolution (DE) (Storn & Price, 1997), evolutionary strategy (ES) (Back, 1996;Beyer, 2001), particle swarm optimization (PSO) (Kennedy & Eberhart, 1995), ant colony optimization (ACO) (Dorigo, 2004), cuckoo search (CS) (Gandomi et al, 2013), firefly algorithm (FA) (Gandomi, 2011), biogeography-based optimization (BBO) (Simon, 2008) big bang-big crunch algorithm (Erol & Eksin, 2006), charged system search (CSS) (Kaveh & Talatahari, 2010) animal migration optimization (AMO) (Li et al, 2013), water cycle algorithm (WCA) (Eskandar et al, 2012), mine blast Algorithm (MBA) (Sadollaha et al, 2013), harmony search algorithm (Mahdavi et al, 2007), improvements of Symbiosis Organisms Search Algorithm (Nama et al 2016b(Nama et al , 2016bNama & Saha, 2018). Recently Civicioglu (2013) proposed a novel algorithm called backtracking search algorithm (BSA) which is based on the return of a social group at random intervals to hunting areas that were previously found fruitful for obtaining nourishment (Civicioglu, 2013;Nama et al, 2016d) BSA's strategy for generating a trial population includes two new crossover and mutation operators which are different from other evolutionary algorithm like DE and GA. BSA uses random mutation strategy with one direction individual for each target individual and a non-uniform crossover strategy.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid backtracking search optimization algorithm for continuous function optimization

Nama¹,

Saha²

2019

10.5267/j.dsl

Self Cite

View full text Add to dashboard Cite

Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed. .

show abstract

“…A number of (meta) heuristic based algorithmic strategies have been proposed in the quest for finding near-optimum solutions to the inventory blood assignment problem, among these algorithms include Hill climbing (HC) [37], [38], Simulated annealing (SA) [37], [38], Genetic algorithm (GA) [3], [36], Tabu search (TS) [39], Particle swarm optimization (PSO) [12], Greedy Randomized Adaptive Search Procedure (GRASP) [13] and Symbiotic organisms search (SOS) algorithm [31], [38]. All the algorithms listed here draw their inspiration from nature, through the observation of physical processes that occur in nature.…”

Section: Introductionmentioning

confidence: 99%

A Symbiotic Organisms Search Algorithm for Optimal Allocation of Blood Products

Govender

Ezugwu

2019

IEEE Access

View full text Add to dashboard Cite

The blood assignment problem is an important real-world optimization problem because of the continuous demand for blood transfusion during medical emergencies. The formulation of this problem stretches from managing critical blood shortage levels and blood unit expiration, to blood compatibility between donor and patients. Another contributing factor to the blood assignment problem lies in the blood bank having to import additional blood units from external sources when supply cannot meet the demand. These challenges have serious consequences especially in the case where the demand for the blood is very high. Therefore, there is the need to minimize blood product wastage with regards to expiration and importation, whilst maximizing product delivery to patients in need. To solve this problem, existing studies used fixed percentage bounds to generate values for demand and supply of whole blood units. However, in this paper, a different approach is considered when generating such values, which involves allocating a unique percentage bound to each month. The bounds conform to statistics taken from South African social behavior with the attempt at generating values which mimic real-life monthly demand and supply of whole blood units. Furthermore, this study implements a hybrid metaheuristic algorithm that combines symbiotic organisms search algorithm with the blood assignment policy in relation to the blood banks of South Africa to effectively minimize the operational costs of the blood transfusion centers. The computational results indicate that the proposed algorithm performed quite satisfactorily from the computational time, stockpiling , and low importation level points of view. INDEX TERMS Blood assignment problem, blood product, blood group, blood compatibility, symbiotic organisms search algorithm, SOS.

show abstract

Improved symbiotic organisms search algorithm for solving unconstrained function optimization

Cited by 46 publications

References 26 publications

Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

A novel hybrid backtracking search optimization algorithm for continuous function optimization

A Symbiotic Organisms Search Algorithm for Optimal Allocation of Blood Products

Contact Info

Product

Resources

About