A clustering-based symbiotic organisms search algorithm for high-dimensional optimization problems

2020 ◽  
Vol 97 ◽  
pp. 106722
Author(s):  
Chao-Lung Yang ◽  
Hendri Sutrisno
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
High Dimensional ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms ◽  
Dimensional Optimization

An enhanced symbiotic organisms search algorithm for design optimization of trusses with frequency constraints

2021 ◽  
pp. 136943322110262
Author(s):  
Mohammad H Makiabadi ◽  
Mahmoud R Maheri
Keyword(s):  
Standard Deviation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Metaheuristic Algorithms ◽  
Minimum Mass ◽  
Multiple Frequency ◽  
Frequency Constraints ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms ◽  
Dynamic Frequency

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems

2019 ◽  
Vol 77 ◽  
pp. 567-583 ◽  
Author(s):  
Khoa H. Truong ◽  
Perumal Nallagownden ◽  
Zuhairi Baharudin ◽  
Dieu N. Vo
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

scholarly journals A New Method for Analyzing the Performance of the Harmony Search Algorithm

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1421 ◽  
Author(s):  
Shouheng Tuo ◽  
Zong Woo Geem ◽  
Jin Hee Yoon
Keyword(s):  
Success Rate ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
High Dimensional ◽  
Multimodal Optimization ◽  
Harmony Memory ◽  
Dimensional Optimization ◽  
Take All

A harmony search (HS) algorithm for solving high-dimensional multimodal optimization problems (named DIHS) was proposed in 2015 and showed good performance, in which a dynamic-dimensionality-reduction strategy is employed to maintain a high update success rate of harmony memory (HM). However, an extreme assumption was adopted in the DIHS that is not reasonable, and its analysis for the update success rate is not sufficiently accurate. In this study, we reanalyzed the update success rate of HS and now present a more valid method for analyzing the update success rate of HS. In the new analysis, take-k and take-all strategies that are employed to generate new solutions are compared to the update success rate, and the average convergence rate of algorithms is also analyzed. The experimental results demonstrate that the HS based on the take-k strategy is efficient and effective at solving some complex high-dimensional optimization problems.

scholarly journals A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

2021 ◽  
Author(s):  
Ajoze Abdulraheem Zubair ◽  
Shukor Bin Abd Razak ◽  
Md. Asri Bin Ngadi
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Scheduling Algorithm ◽  
Geometric Mean ◽  
Optimal Solution ◽  
Distinct Species ◽  
Scheduling Problems ◽  
Or Efficiency ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.

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