Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization

Kaur, Satnam; Awasthi, Lalit Kumar; Sangal, Amrit Lal; Dhiman, Gaurav

doi:10.1016/j.engappai.2020.103541

Cited by 912 publications

(515 citation statements)

References 36 publications

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“…Where, is the sorted matrix of algorithm population, 1 is the member with best fitness value, is the member with worst fitness value,…”

Section: Multi-leader Optimizer (Mlo)mentioning

confidence: 99%

“…There are different methods for solving optimization problems. Optimization algorithms with high power in solving optimization problems are always considered as one of the effective methods of solving optimization problems [1]. In this regard, optimization algorithms have been applied by scientists in various fields such as energy [2], protection [3], distribution systems [4,5], storage designing [6], electrical engineering [7,8], energy commitment [9], and energy carriers [10,11] algorithms with random search in problem solving space can provide a suitable solution for a problem [12].…”

Section: Introductionmentioning

confidence: 99%

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MLO: Multi Leader Optimizer

Dehghani¹,

Montazeri²,

Dehghani³

et al. 2020

IJIES

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Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

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“…Where, is the sorted matrix of algorithm population, 1 is the member with best fitness value, is the member with worst fitness value,…”

Section: Multi-leader Optimizer (Mlo)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MLO: Multi Leader Optimizer

Dehghani¹,

Montazeri²,

Dehghani³

et al. 2020

IJIES

Self Cite

View full text Add to dashboard Cite

show abstract

“…The standard tunicate swarm algorithm is very simple bioinspired meta-heuristic optimization algorithm that was firstly proposed by S. Kaur et al in 2020 [44]. Its inspiration and performance were effectively proven over seventy-four benchmark problems compared with several other optimization approaches.…”

Section: A Tunicate Swarm Algorithm (Tsa)mentioning

confidence: 99%

“…These variety of the control variables and complexity of introducing a fully automated distribution system by optimal control of the DNR, CBs and DGs simultaneously makes an urgent need to introduce an efficient technique to solve this problem. Recently, Tunicate Swarm Algorithm (TSA) is a bioinspired meta-heuristic optimizer algorithm that is firstly proposed by S. Kaur et al in 2020 [44]. Its inspiration and performance are effectively proven over seventy-four benchmark problems compared with several other optimization approaches.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

Fetouh

Elsayed

2020

IEEE Access

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Simultaneous control and operation of Capacitor Banks (CBs) and Distributed Generators (DGs) with Distribution Network Reconfiguration (DNR) is the most important modern trend for distribution systems performance enhancement. The complexity and variety of the control variables of this problem represent a challenge for distribution systems planners and operators as well as interested researchers. Optimal solution can provides quantitative as well as qualitative power service to satisfy consumers' satisfaction and reduces dissipated energy. Furthermore, performance enhancement of distribution systems is very vital where improving their performance directly affects the performance of transmission and generation systems. For solving the considered problem, an improved Tunicate Swarm Algorithm (ITSA), which imitates the swarming behaviors of the marine tunicates and their jet propulsions during its navigation and foraging procedure, is proposed. In ITSA, Lévy flight distributions are emerged in the traditional Tunicate Swarm Algorithm (TSA), which improves the diversification searching abilities of the TSA and consequently avoids the stagnation possibilities. The proposed ITSA is applied and tested for optimal DNR simultaneously with optimal control of the switched CBs and dispatchable DGs taking into account daily load variations. The ITSA is compared with other techniques on the standard 33-bus, 69-bus and the large-scale 119-bus distribution systems considering different automation scenarios of the distribution systems. The simulation results reveal that significant enhancement in distribution system performance is obtained through the application of the proposed automation process using the ITSA. ITSA can efficiently search for the optimal solutions of the problem and outperforms the other existing algorithms in the literatures.

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“…In this section, the performance of DPO is evaluated on a standard set of benchmark test functions which have been used by the researchers in various earlier studies [55,56]. These benchmark functions includes twenty-three test functions that are categorized into Unimodal [57,58], Multimodal [58,59], and Fixed-dimension Multimodal [58] functions.…”

Section: Case Study A: Benchmark Test Functionsmentioning

confidence: 99%

A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem

et al. 2020

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Regular assessments of events taking place around the globe can be a conduit for the development of new ideas, contributing to the research world. In this study, the authors present a new optimization algorithm named doctor and patient optimization (DPO). DPO is designed by simulating the process of treating patients by a physician. The treatment process has three phases, including vaccination, drug administration, and surgery. The efficiency of the proposed algorithm in solving optimization problems compared to eight other optimization algorithms on a benchmark standard test function with 23 objective functions is been evaluated. The results obtained from this comparison indicate the superiority and quality of DPO in solving optimization problems in various sciences. The proposed algorithm is successfully applied to solve the energy commitment problem for a power system supplied by a multiple energy carriers system.

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Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization

Cited by 912 publications

References 36 publications

MLO: Multi Leader Optimizer

MLO: Multi Leader Optimizer

Optimal Control and Operation of Fully Automated Distribution Networks Using Improved Tunicate Swarm Intelligent Algorithm

A New “Doctor and Patient” Optimization Algorithm: An Application to Energy Commitment Problem

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