Firefly Algorithm with non-homogeneous population: A case study in economic load dispatch problem

Kapelinski, Kléber; Neto, João Plínio Juchem; Santos, Eduardo Machado dos

doi:10.1080/01605682.2019.1700184

Cited by 10 publications

(5 citation statements)

References 40 publications

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“…The results of statistical comparisons between CO and different meta-heuristic and improved algorithms in recent studies are summarized in Table 9 . The competitive modified and hybrid algorithms in these two cases are: bacterial foraging optimization (BFO) 76 , a modified ion motion optimization (MIMO) conglomerated with crisscross search (CSO) named C-MIMO-CSO 77 , TLBO 78 , an improved orthogonal design particle swarm optimization (IODPSO) algorithm 79 , synergic predator–prey optimization (SPPO) algorithm 80 , multi-strategy ensemble biogeography-based optimization (MsEBBO) 81 , a new variant for the firefly algorithm, considering a non-homogeneous population named NhFA-Rnp 82 , clustering cuckoo search optimization (CCSO) 83 , a novel variant of competitive swarm optimizer (CSO) referred to as OLCSO 84 , and adaptive charged system search (ACSS) 85 . The results demonstrate that the algorithm outperforms other state-of-the-art and improved algorithms in terms of worst, mean, best, and standard deviation values.…”

Section: Simulation Resultsmentioning

confidence: 99%

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

Akbari

Zare

Azizipanah‐Abarghooee

et al. 2022

Sci Rep

127

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Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co.

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Section: Simulation Resultsmentioning

confidence: 99%

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

Akbari

Zare

Azizipanah‐Abarghooee

et al. 2022

Sci Rep

127

View full text Add to dashboard Cite

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“…The evaluation experiments implied superiority of the proposed ISPSO over other models used in the evaluation experiments. Another approach to solve an ELD problem in [21], is the proposing of a new variation of the firefly optimization algorithm with non-homogeneous population. An increase in the population from 4 to 4 × n and two population initialization techniques were used.…”

Section: Previous Methods Of Eldmentioning

confidence: 99%

A Hybrid Capuchin Search Algorithm with Gradient Search Algorithm for Economic Dispatch Problem

Braik

Awadallah

Al-Betar

et al. 2022

Preprint

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Economic Load Dispatch (ELD) is an important task in power systems that is concerned with scheduling a set of thermal generating units to produce specific power at low consumption costs. In this task, there are some operational constraints related to power demand, power output, valve-point loading effect, transmission losses, prohibited operating zones, and ramp-rate limits. ELD is drafted as a non-linear, non-convex, and highly constrained optimization problem with a large number of local minima. Due to the complex and rugged search space of this problem, it has been tackled by many meta-heuristic algorithms. To cope with the above issues of this problem, we presented a novel Improved based Hybrid Capuchin Search Algorithm (IHCSA). First, a memory element was added to CSA to consolidate its position and velocity updating mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive functions rather than stationary social and cognitive parameters were used to manage and adequately balance exploration and exploitation of features over the iterations path. Finally, the hybridization was carried out using Gradient Search Algorithm (GBO) and Local Escaping Capability (LEO) to strengthen the intensification ability of CSA and balance its search capabilities to fulfill robust search performance. The proficiency of IHCSA was divulged by assessing it on a computationally difficult ELD problem under 6 different tests with different numbers of generators and loading conditions. The proposed IHCSA provided the best performance, in terms of the minimum cost, among the basic CSA and other optimization methods.

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“…The test findings for the suggested approach showed high convergence features and low generation costs, making them extremely effective and encouraging. [42] ELD problems were solved with the FA. A 15-unit ELD problem with many considerations for each generator was solved using ten benchmark functions, and a 13-unit non-convex system with a valve-point loading effect was solved.…”

Section: Yearmentioning

confidence: 99%

“…Recently, many optimization algorithms, such as the white shark optimizer [10], the search and rescue optimization algorithm (SAR) [11], the greedy sine-cosine nonhierarchical gray wolf optimizer (G-SCNHGWO) [12], the efficient chameleon swarm algorithm (CSA) [13], the memetic sine cosine algorithm [14], the hybrid Harris hawks optimizer (HHO) [15], the oppositional pigeon-inspired optimizer (OPIO) algorithm [16], the modified krill herd algorithm [17], the modified differential evolution algorithm [18], artificial eco system-based optimization [19], turbulent flow of water optimization (TFWO) [20], particle swarm optimization (PSO) [21], evolution strategy (ES) [22], teaching learning based optimization (TLBO) [23], the modified symbiotic organisms search algorithm (MSOS) [24], civilized swarm optimization (CSO) [25], the ant lion optimization algorithm (ALO) [26], the efficient distributed auction optimization algorithm (DAOA) [27], the hybrid grey wolf optimizer (HGWO) [28], the improved genetic algorithm (IGA) [29], the improved firefly algorithm (IFA) [30], biogeography-based optimization (BBO) [31], the heat transfer search (HTS) algorithm [32], adaptive charged system search (ACSS) [33], the evolutionary simplex adaptive Hooke-Jeeves algorithm (ESAHJ) [34], the enhanced moth-flame optimizer (EMFO) [35], multi-strategy ensemble biogeography-based optimization (MSEBBO) [36], several new hybrid algorithms [37], a fully decentralized approach (DA) [38], the exchange market algorithm (EMA) [39], bacterial foraging optimization (BFO) [40], the artificial cooperative search algorithm (ACS) [41], a new firefly algorithm (FA) via a non-homogeneous population [42]<...…”

Section: Introductionmentioning

confidence: 99%

Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem

Ismaeel,

Houssein,

Khafaga

et al. 2023

Mathematics

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The osprey optimization algorithm (OOA) is a new metaheuristic motivated by the strategy of hunting fish in seas. In this study, the OOA is applied to solve one of the main items in a power system called economic load dispatch (ELD). The ELD has two types. The first type takes into consideration the minimization of the cost of fuel consumption, this type is called ELD. The second type takes into consideration the cost of fuel consumption and the cost of emission, this type is called combined emission and economic dispatch (CEED). The performance of the OOA is compared against several techniques to evaluate its reliability. These methods include elephant herding optimization (EHO), the rime-ice algorithm (RIME), the tunicate swarm algorithm (TSA), and the slime mould algorithm (SMA) for the same case study. Also, the OOA is compared with other techniques in the literature, such as an artificial bee colony (ABO), the sine cosine algorithm (SCA), the moth search algorithm (MSA), the chimp optimization algorithm (ChOA), and monarch butterfly optimization (MBO). Power mismatch is the main item used in the evaluation of the OOA with all of these methods. There are six cases used in this work: 6 units for the ELD problem at three different loads, and 6 units for the CEED problem at three different loads. Evaluation of the techniques was performed for 30 various runs based on measuring the standard deviation, minimum fitness function, and maximum mean values. The superiority of the OOA is achieved according to the obtained results for the ELD and CEED compared to all competitor algorithms.

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Firefly Algorithm with non-homogeneous population: A case study in economic load dispatch problem

Cited by 10 publications

References 40 publications

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems

A Hybrid Capuchin Search Algorithm with Gradient Search Algorithm for Economic Dispatch Problem

Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem

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