Ant Lion Optimization: Variants, Hybrids, and Applications

Assiri, Adel; Hussien, Abdelazim G.; Amin, Mohamed

doi:10.1109/access.2020.2990338

Cited by 124 publications

(58 citation statements)

References 178 publications

(107 reference statements)

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“…In literature, enormous types of optimization algorithms has been proposed in the last decades such as Genetic Algorithm (GA) [20], Simulated Annealing (SA) [21], Tabu Search (TS) [22], Particle Swarm Optimization [23], Differential Search Algorithm (DSA) [24], Harmony Search (HS) [25], Cat Swarm Optimization (CSO) [26], Firefly Algorithm (FA) [27], Cuckoo Search (CS) [28], Gravitational Search Algorithm (GSA) [29], Virus Optimization Algorithm (VOA) [30], Bat Algorithm (BA) [31], Ant Colony Optimization (ACO) [32], Flower Pollination Algorithm (FPA) [33], Krill Herd (KH) Algorithm [34], Chicken Swarm Optimization (CSO) [35], Grey Wolf Optimizer (GWO) [36], Social Spider Algorithm (SSA) [37], Ant Lion Optimizer (ALO) [38], Moth-Flame Optimization (MFO) [39], Elephant Herding (EH) Optimization [40], Multi-Verse Optimizer (MVO) [41], Whale Optimization Algorithm (WOA) [42], Dragonfly Algorithm (DA) [43], Sine Cosine Algorithm (SCA) [44], Kidney-Inspired Algorithm [45], Spotted Hyena Optimizer (SHO) [46], Grasshopper Optimization Algorithm (GOA) [47], Salp Swarm Algorithm (SSA) [48], Thermal Exchange Optimization [56], Squirrel Search Algorithm [58], Henry Gas Solubility Optimization (HGSO) [59], Harris Hawks Optimization (HHO) [60], Nuclear Reaction Optimization (NRO) [61]. In literature, there are many metaheuristics algorithms classification.…”

Section: Metaheuristicsmentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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Section: Metaheuristicsmentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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“…Thus, ALO has been employed in solving many optimization problems [35]- [37]. However, ALO succeeded in solving several optimization problems; it suffers from stagnation in some cases.…”

Section: Introduction a Literature Surveymentioning

confidence: 99%

Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM Under Uncertainties of Loads and Solar Irradiance

et al. 2021

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Optimal planning of integration the Photovoltage Distributed Generation (PV-DG) and DSTATCOM is a crucial task due to the stochastic variations of PV output power and the load demand which are related to solar irradiance variations and the activities of the customers, respectively. In this article, the optimal planning problem of the PV-DG and DSTATCOM system is solved. The proposed model considers the uncertainties of the solar irradiance and the load demand for a multi-objective function, including the cost reduction, the voltage profile, and stability index improvement. Modified Ant Lion Optimizer (MALO) is proposed to enhance the basic ALO searching ability using two strategies. The first strategy is based on Levy Flight Distribution (LFD) to strengthen the exploration of the algorithm and avoid the premature of the basic ALO. In contrast, the second strategy is based on updating the solutions in a spiral orientation to improve the exploitation of the algorithm. The IEEE 69-bus and 118-bus radial distribution systems are used to demonstrate the effectiveness of the proposed method, and the yielded simulations are compared with the basic ALO and other well-known optimization techniques for power loss minimization under deterministic conditions. The simulation results demonstrate that the techno-economic benefits can be increased considerably by optimal inclusion of two PV-DGs and DSTATCOMs compared with a single system.

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“…Though, MOV suffers from slow searching, local minima, and premature convergence [63,48]. Parameter Three [17] Three [63] Three [51] Five [53] Three [16] Four [77] Complexity O(nlogn) [43] O(n.D.tmax) [57] O(m 2 ) [9] O(nm 2 ) [73] O(HM S × M + HM S × log(HM S)) [72] O(mn 2 ) [55] Convergence Smooth convergence with fast rate [43] Slow convergence rate [58] Fast convergence [74] Quickly converge [45] Suffer from premature convergence [24] Rapidly converged [77] Strength Balance between exploration and exploitation [30] Balance between intensification and diversification [3] Deal with the complex fitness landscape [29] Don't have overlapping and mutation calculation [5] Increases the diversity of the new solutions [13] Avoid trapped at local optimum [36] Weaknesses Relaxed convergence [31] Trapped in a local optimum [65] Evaluation is relatively expensive [78] Suffers from partial optimism [15] Get stuck on local optima [46] Needs huge memory resources [36] Therefore, there are several methods applied to solve these drawbacks including,the enhanced MVO proposed in [10], the authors improved the basic MVO by introducing a new version, called EMVO to achieve high accuracy and efficiency of the requirement prioritization. EMVO based on exchanging the information between the current solutions.…”

Section: Introductionmentioning

confidence: 99%

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

Shehab¹,

Abualigah

2021

Preprint

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Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

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Ant Lion Optimization: Variants, Hybrids, and Applications

Cited by 124 publications

References 178 publications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Crow Search Algorithm: Theory, Recent Advances, and Applications

Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM Under Uncertainties of Loads and Solar Irradiance

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

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