scholarly journals A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems

2022 ◽  
Vol 130 (1) ◽  
pp. 331-364
Author(s):  
Andi Tang ◽  
Huan Zhou ◽  
Tong Han ◽  
Lei Xie
Keyword(s):  
Search Algorithm ◽  
Logarithmic Spiral ◽  
Engineering Problems ◽  
Adaptive Step

scholarly journals Adaptive Cuckoo Search Algorithm for Unconstrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Pauline Ong
Keyword(s):  
Marked Improvement ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimal Solutions ◽  
Step Size ◽  
Adjustment Strategy ◽  
Global Optimal ◽  
Adaptive Step ◽  
Size Adjustment

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

PSO-based group-oriented crow search algorithm (PGCSA)

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudeepa Das ◽  
Tirath Prasad Sahu ◽  
Rekh Ram Janghel
Keyword(s):  
Design Methodology ◽  
Search Algorithm ◽  
Search Space ◽  
Benchmark Problems ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Content Type ◽  
Engineering Problems ◽  
Novel Approaches ◽  
Two Phases

Purpose The purpose of this paper is to modify the crow search algorithm (CSA) to enhance both exploration and exploitation capability by including two novel approaches. The positions of the crows are updated in two approaches based on awareness probability (AP). With AP, the position of a crow is updated by considering its velocity, calculated in a similar fashion to particle swarm optimization (PSO) to enhance the exploiting capability. Without AP, the crows are subdivided into groups by considering their weights, and the crows are updated by conceding leaders of the groups distributed over the search space to enhance the exploring capability. The performance of the proposed PSO-based group-oriented CSA (PGCSA) is realized by exploring the solution of benchmark equations. Further, the proposed PGCSA algorithm is validated over recently published algorithms by solving engineering problems. Design/methodology/approach In this paper, two novel approaches are implemented in two phases of CSA (with and without AP), which have been entitled the PGCSA algorithm to solve engineering benchmark problems. Findings The proposed algorithm is applied with two types of problems such as eight benchmark equations without constraint and six engineering problems. Originality/value The PGCSA algorithm is proposed with superior competence to solve engineering problems. The proposed algorithm is substantiated hypothetically by using a paired t-test.

scholarly journals Pseudodynamic Bearing Capacity Analysis of Shallow Strip Footing Using the Advanced Optimization Technique “Hybrid Symbiosis Organisms Search Algorithm” with Numerical Validation

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Arijit Saha ◽  
Apu Kumar Saha ◽  
Sima Ghosh
Keyword(s):  
Bearing Capacity ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Seismic Loading ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Strip Footing ◽  
Composite Failure ◽  
Engineering Problems ◽  
Dynamic Modules

The analysis of shallow foundations subjected to seismic loading has been an important area of research for civil engineers. This paper presents an upper-bound solution for bearing capacity of shallow strip footing considering composite failure mechanisms by the pseudodynamic approach. A recently developed hybrid symbiosis organisms search (HSOS) algorithm has been used to solve this problem. In the HSOS method, the exploration capability of SQI and the exploitation potential of SOS have been combined to increase the robustness of the algorithm. This combination can improve the searching capability of the algorithm for attaining the global optimum. Numerical analysis is also done using dynamic modules of PLAXIS-8.6v for the validation of this analytical solution. The results obtained from the present analysis using HSOS are thoroughly compared with the existing available literature and also with the other optimization techniques. The significance of the present methodology to analyze the bearing capacity is discussed, and the acceptability of HSOS technique is justified to solve such type of engineering problems.

scholarly journals A Complex-Valued Encoding Multichain Seeker Optimization Algorithm for Engineering Problems

2022 ◽  
Vol 2022 ◽  
pp. 1-35
Author(s):  
Shaomi Duan ◽  
Huilong Luo ◽  
Haipeng Liu
Keyword(s):  
Optimization Algorithm ◽  
Pid Control ◽  
Control Parameter ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Local Optimum ◽  
Engineering Problems ◽  
Complex Valued ◽  
Seeker Optimization Algorithm

This article comes up with a complex-valued encoding multichain seeker optimization algorithm (CMSOA) for the engineering optimization problems. The complex-valued encoding strategy and the multichain strategy are leaded in the seeker optimization algorithm (SOA). These strategies enhance the individuals’ diversity, enhance the local search, avert falling into the local optimum, and are the influential global optimization strategies. This article chooses fifteen benchmark functions, four proportional integral derivative (PID) control parameter models, and six constrained engineering problems to test. According to the experimental results, the CMSOA can be used in the benchmark functions, in the PID control parameter optimization, and in the optimization of constrained engineering problems. Compared to the particle swarm optimization (PSO), simulated annealing based on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multiverse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of the CMSOA are better than those of others algorithms.

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