Enhancing tree-seed algorithm via feed-back mechanism for optimizing continuous problems

Jiang, Jianhua; Meng, Xianqiu; Chen, Yunjun; Qiu, Chunyan; Liu, Yang; Li, Keqin

doi:10.1016/j.asoc.2020.106314

Cited by 19 publications

(8 citation statements)

References 59 publications

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“…Table 8 Comparison results of the proposed GABC/ds algorithm and state of the art algorithms in terms of average error in objective function value for 30 dimension of CEC2014 test problems F GSA [22,23,44] CS [22,23,58] LX-BBO [22,20] B-BBO [22,20] SOS [22,23,8] RW-GWO [22] GWO [22] OHDA [21] fb TSA [26] Total GSA [22,23,44] 3-0-0 12-0-1 3-0-3 3-0-5 21-0-9 CS [22,23,58] 2-0-1 12-0-1 2-0-4 7-0-1 23-0-7 LX-BBO [22,20] 3-0-0 11-0-2 5-0-1 6-0-2 25-0-5 B-BBO [22,20] 3-0-0 11-0-2 4-0-2 6-0-2 24-0-6 SOS [22,23,8] 3-0-0 13-0-0 6-0-0 8-0-0 30-0-0 RW-GWO [22] 3-0-0 13-0-0 3-0-3 1-2-5 20-2-8 GWO [22] 3-0-0 13-0-0 5-0-1 6-1-1 27-1-2 OHDA [21] 2-0-1 12-0-1 3-0-3 4-1-3 21-1-8 fb TSA [26] 3-0-0 8-0-5 2-0-4 5-1-2 18-1-11 BFL-PSO [6] 1-0-2 9-0-4 0-0-6 4-2-2 14-2-14 SDPSO [42] 1-0-2 11-0-2 2-0-4 1-1-6 15-1-14…”

Section: Proposed Models For Solving Cec2015 Benchmark Functionsmentioning

confidence: 99%

Artificial Bee Colony Algorithm with Directed Scout

Saleh

Akay

2021

Preprint

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As a relatively new model, the Artificial Bee Colony Algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modify the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on the CEC2014 benchmark suite, CEC2015 benchmark functions, and three real-life problems: pressure vessel design problem, tension and compression spring design problem, and Frequency-Modulated (FM) problem. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results.

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Section: Proposed Models For Solving Cec2015 Benchmark Functionsmentioning

confidence: 99%

Artificial Bee Colony Algorithm with Directed Scout

Saleh

Akay

2021

Preprint

View full text Add to dashboard Cite

show abstract

Section: Proposed Models For Solving Cec2015 Benchmark Functionsmentioning

confidence: 99%

Artificial bee colony algorithm with directed scout

Saleh

Akay

2021

Soft Comput

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As a relatively new model, the Artificial Bee Colony Algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modify the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on the CEC2014 benchmark suite, CEC2015 benchmark functions, and three real-life problems: pressure vessel design problem, tension and compression spring design problem, and Frequency-Modulated (FM) problem. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the stateof-the-art optimization algorithms, and it produced competitive results.

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“…It consists of five variables, three equality constraints and six inequality constraints. The specific forms are as follows: Several researchers have employed different algorithms to solve this problem, such as SSA [45], RW -GWO [46], hybrid GSA -GA [47], AQPSO [48], fb_TSA [49], and DGWO [50]. Table 8 shows the best results obtained from the CISO method compared to the previously mentioned methods.…”

Section: B Himmelblau's Optimization Problemmentioning

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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Enhancing tree-seed algorithm via feed-back mechanism for optimizing continuous problems

Cited by 19 publications

References 59 publications

Artificial Bee Colony Algorithm with Directed Scout

Artificial Bee Colony Algorithm with Directed Scout

Artificial bee colony algorithm with directed scout

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

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