Analysing the effects of various switching probability characteristics in flower pollination algorithm for solving unconstrained function minimization problems

Özsoydan, Fehmi Burçin; Baykasoğlu, Adil

doi:10.1007/s00521-018-3602-2

Cited by 20 publications

(4 citation statements)

References 29 publications

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“…Among them are bee colony optimization [15] [16] based on the foraging behaviour of bees and waggle dance communicating food source to the colony [17], firefly algorithm [18] based on the short and rhythmic flashing used for signalling to attract each other, glowworms swarm optimization [19] simulating the movement of the glowworms based on the distance between them and the luminescent quantity, and bat algorithm [20] based on echolocation behaviour of bats. Flower pollination algorithm [21], [22] based on biological evolution of pollination of the flowers, bacterial colony foraging [23] [24] based on biomimicry of foraging bacteria, and salp swarm algorithm [25] enthused from salp navigating and foraging behaviour in oceans are inspirations from the microscopic organisms. Recently, living in groups and hunting behaviours of animals have been used to develop clever SI-based optimization techniques such as grey wolf optimization [26], group search optimizer [27], and spider monkey optimization [28].…”

Section: Introductionmentioning

confidence: 99%

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Akhand

Ayon

Shahriyar

et al. 2020

Applied Soft Computing

157

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Meta-heuristic algorithms inspired by biological species have become very popular in recent years. Collective intelligence of various social insects such as ants, bees, wasps, termites, birds, fish, has been investigated to develop a number of meta-heuristic algorithms in the general domain of swarm intelligence (SI). The developed SI algorithms are found effective in solving different optimization tasks. Traveling Salesman Problem (TSP) is the combinatorial optimization problem where a salesman starting from a home city travels all the other cities and returns to home city in the shortest possible path. TSP is a popular problem due to the fact that the instances of TSP can be applied to solve real-world problems, implication of which turns TSP into a standard test bench for performance evaluation of new algorithms. Spider Monkey Optimization (SMO) is a recent addition to SI algorithms based on the social behavior of spider monkeys. SMO implicitly adopts grouping and regrouping for the interactions to improve solutions; such multi-population approach is the motivation of this study to develop an effective method for TSP. This paper presents an effective variant of SMO to solve TSP called discrete SMO (DSMO). In DSMO, every spider monkey represents a TSP solution where Swap Sequence (SS) and Swap Operator (SO) based operations are employed, which enables interaction among monkeys in obtaining the optimal TSP solution. The SOs are generated using the experience of a specific spider monkey as well as the experience of other members (local leader, global leader, or a randomly selected spider monkey) of the group. The performance and effectiveness of the proposed method have been verified on a large set of TSP instances and the outcomes are compared to other well-known methods. Experimental results demonstrate the effectiveness of the proposed DSMO for solving TSP.

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Section: Introductionmentioning

confidence: 99%

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Akhand

Ayon

Shahriyar

et al. 2020

Applied Soft Computing

157

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“…Thus, one of the disadvantages of MRFOA, which is the weakening of the ability to benefit due to random reference location selection in early iterations, is prevented. For this reason, different studies have been carried out on the nonlinear use of this parameter in the literature [24,40]. This study proposes the use of the simulated annealing inertia weight strategy [41] for parameter 𝐶.…”

Section: Enhanced Manta Ray Foraging Optimization Algorithm (Emrfo)mentioning

confidence: 99%

Performance Evaluations of the Manta Ray Foraging Optimization Algorithm in Real-World Constrained Optimization Problems

Yıldızdan

2024

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Metaheuristic algorithms are often preferred for solving constrained engineering design optimization problems. The most important reason for choosing these algorithms is that they guarantee a satisfactory response within a reasonable time. The swarm intelligence-based manta ray foraging optimization algorithm (MRFO) is a metaheuristic algorithm proposed to solve engineering applications. In this study, the performance of MRFO is evaluated on 19 mechanical engineering optimization problems in the CEC2020 real-world constrained optimization problem suite. In order to increase the MRFO performance, three modifications are made to the algorithm; in this way, the enhanced manta ray foraging optimization (EMRFO) algorithm is proposed. The effects of the modifications made are analyzed and interpreted separately. Its performance has been compared with the algorithms in the literature, and it has been shown that EMRFO is a successful and preferable algorithm for this problem suite.

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“…Several researchers in their research studies have adopted such similar effective techniques to identify the appropriate solution for optimum power production in order to ensure sustainable power flow through transmission corridors. PSO [19], Gravitational Search Algorithm (GSA) [33] ABC [28], FFA [22], Flower Pollination Algorithm (FPA) [34], Cuckoo Search Algorithm (CSA) [35], and improved Crow Search Algorithm (CSA) [36] are some of such approaches implemented to supervise the optimal power system operations. Suitable assortment and adjustment of the control parameters related to the meta heuristic techniques has a substantial impact on the solution achieved with its application.…”

Section: A Literature Surveymentioning

confidence: 99%

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

Paul¹,

Sinha

Bouteraa

et al. 2023

IEEE Access

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This research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve the most influential power system generators that will reschedule their real power to alleviate the excess power flow in congested transmission lines. Secondly, the IMRFO has been developed and applied to attain the minimum possible congestion cost. The IMRFO has been formulated with the inclusion of correction factors in the exploration and exploitation phases to improve the coordination between these phases. The effectiveness of IMRFO has been measured considering its effective performance on the 23 conventional benchmark functions. 39 bus New England and IEEE-118 bus test system has been utilized to authenticate the effectiveness of the CM approach with the application of IMRFO. The outcomes highlight that the congestion cost achieved with IMRFO has been reduced by, of 16.08%, 13.73%, 11.78%, and 4.48 % for the 39-bus system and 14.84%, 12.97%, 9.63%, and 6.85% for 118 bus system when compared to the Bacteria Forge Optimization (BFO), Grey Wolf Optimization (GWO), Sine-Cosine Algorithm (SCA), and Original MRFO. The results gained with the implementation of IMRFO on the CM problem portrays appreciable minimization in the congestion cost, enhancement in the system voltage and losses, generates better convergence profile and computational time when contrasted with the recent optimization methods.INDEX TERMS Manta ray forge optimization, meta-heuristic technique, optimal power flow, optimization, power rescheduling, sensitivity analysis.

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Analysing the effects of various switching probability characteristics in flower pollination algorithm for solving unconstrained function minimization problems

Cited by 20 publications

References 29 publications

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Discrete Spider Monkey Optimization for Travelling Salesman Problem

Performance Evaluations of the Manta Ray Foraging Optimization Algorithm in Real-World Constrained Optimization Problems

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

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