Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem

Obagbuwa, Ibidun Christiana; Abidoye, Ademola P.

doi:10.3390/a9030059

Cited by 12 publications

(4 citation statements)

References 22 publications

(33 reference statements)

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“…where c 1 and c 2 are positive constant (individual and global) learning rates, while r 1 and r 2 are random values between 0 and 1. χ is known as the constriction factor and has the following defnition [51]: An all-inclusive diagrammatic representation of an ANFIS-based MPPT controller trained using a PSO is illustrated in Figure 7. Before performing error reduction, a cutting-edge hybrid ANFIS approach collects fuzzy data by modifying membership values according to learning criteria.…”

Section: Proposed Mppt Methodmentioning

confidence: 99%

“…Te selected algorithms are appraised based on their simplicity of application and recentness (BSOA [49]), and substantial benefts, such as low parameter count, quickly converge, and resistance to being "stuck" in a local optimal solution (SSA [50]). CSO is simple and efcient, solving global optimization issues [51]. Te energy Internet uses TingSpeak to track signifcant discoveries.…”

Section: Paper Contributionsmentioning

confidence: 99%

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Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

Jasim,

Alhasnawi

et al. 2023

International Transactions on Electrical Energy Systems

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High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, generation conditions. The adopted MG regulates the bidirectional DC/AC main converter using digital proportional resonant controllers in a synchronous reference frame. A maximum power point tracker-based boost DC/DC converter enables the wind turbine and solar photovoltaic to harvest maximum power. Traditional methods such as perturb and observe and incremental conductance maximum power trackers cannot solve nonlinearity and inaccurate responses. This work provides a hybrid maximum power tracker strategy to modify the responses of standard maximum power point techniques based on particle swarm optimization-trained adaptive neuro-fuzzy inference system (ANFIS-PSO) to achieve quick and maximum solar power with minimal oscillation tracking. Concerning the management system, this paper adopts a recent meta-heuristic algorithms-based DSM program to modify consumers’ electricity use by shifting the load appliances to off-peak demand periods. The adopted algorithms for DSM are sparrow search algorithm (SSA), binary orientation search algorithm (BSOA), and cockroach algorithm (CA). Finally, based on energy Internet technology, ThingSpeak cloud-based MATLAB is adopted to gather and display real-time data streams and generate graphical analyses. The simulation results reveal that the recommended coordinating control produces quick grid frequency responsiveness and zero steady-state errors. The optimal demand management program minimizes peak energy consumption from 5.2 kWh to 4.6 kWh. All DSM methods cost 439.1 $ per month, compared to 484.4 $ for the nonscheduling load profile.

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Section: Proposed Mppt Methodmentioning

confidence: 99%

Section: Paper Contributionsmentioning

confidence: 99%

Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

Jasim,

Alhasnawi

et al. 2023

International Transactions on Electrical Energy Systems

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“…Statistical results are reported in Table 6. CISO was compared with PSO [10], CSO [31], and GOA [21].…”

Section: Tests Performance Of Algorithmmentioning

confidence: 99%

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Akkar¹,

Salman²

2020

IJIES

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This paper proposes a new meta-heuristic swarm optimization algorithm called Cicada Swarm Optimization (CISO) algorithm, which mimics the behavior of bio-inspired swarm optimization methods. The CISO algorithm is tested with 23 benchmark functions and taken two problems engineering design, pressure vessel problem and himmelblau’s problem. The performance of CISO algorithm is compared with meta-heuristic well-known and recently proposed algorithms (Cockroach Swarm Optimization (CSO), Grasshopper Optimization algorithm (GOA) and Particle Swarm Optimization (PSO)). The obtained results showed that the proposed algorithm succeeded in improving the test functions and solved engineering design problems that could not be improved by other algorithms according to the chosen parameters and the limits of the research space, also showed that CISO has a faster convergence with the minimum number of iterations and also have an accurate calculation efficiency Satisfactory compared to other optimization algorithms.

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“…Due to the increase in metaheuristic algorithms, the number of binary versions of algorithms has increased. A version of the cockroach swarm optimization was proposed in [ 40 ]. The binary bat algorithm was proposed in [ 41 ].…”

Section: Introductionmentioning

confidence: 99%

Binary Sand Cat Swarm Optimization Algorithm for Wrapper Feature Selection on Biological Data

Seyyedabbasi

2023

Biomimetics

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In large datasets, irrelevant, redundant, and noisy attributes are often present. These attributes can have a negative impact on the classification model accuracy. Therefore, feature selection is an effective pre-processing step intended to enhance the classification performance by choosing a small number of relevant or significant features. It is important to note that due to the NP-hard characteristics of feature selection, the search agent can become trapped in the local optima, which is extremely costly in terms of time and complexity. To solve these problems, an efficient and effective global search method is needed. Sand cat swarm optimization (SCSO) is a newly introduced metaheuristic algorithm that solves global optimization algorithms. Nevertheless, the SCSO algorithm is recommended for continuous problems. bSCSO is a binary version of the SCSO algorithm proposed here for the analysis and solution of discrete problems such as wrapper feature selection in biological data. It was evaluated on ten well-known biological datasets to determine the effectiveness of the bSCSO algorithm. Moreover, the proposed algorithm was compared to four recent binary optimization algorithms to determine which algorithm had better efficiency. A number of findings demonstrated the superiority of the proposed approach both in terms of high prediction accuracy and small feature sizes.

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Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem

Cited by 12 publications

References 22 publications

Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Binary Sand Cat Swarm Optimization Algorithm for Wrapper Feature Selection on Biological Data

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