A novel PGSA–PSO hybrid algorithm for structural optimization

Abstract

Purpose The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and particle swarm optimization hybrid algorithm (PGSA–PSO hybrid algorithm), for solving structural optimization problems. Design/methodology/approach To further enhance the optimization efficiency and precision of this algorithm, the optimization solution process of PGSA–PSO comprises two steps. First, an excellent initial growth point is selected by PSO. Then, the global optimal solution can be obtained quickly by PGSA and its improved strategy called growth space adjustment strategy. A typical mathematical example is provided to verify the capacity of the new hybrid algorithm to effectively improve the global search capability and search efficiency of PGSA. Moreover, PGSA–PSO is applied to the optimization design of a suspended dome structure. Findings Through typical mathematical example, the improved strategy can improve the optimization efficiency of PGSA considerably, and an initial growth point that falls near the global optimal solution can be obtained. Through the optimization of the pre-stress of a suspended dome structure, compared with other methods, the hybrid algorithm is effective and feasible in structural optimization. Originality/value Through the examples of suspended dome structure, it shows that the optimization efficiency and precision of PGSA–PSO are better than those of other algorithms and methods. PGSA–PSO is effective and feasible in structural optimization problems such as pre-stress optimization, size optimization, shape optimization and even topology optimization.

A Review of Optimization Algorithms for University Timetable Scheduling

“…Theoretically, individuals can benefit from the discoveries and past experiences of their colleagues when searching for food [56]. This advantage can become critical and outweigh the disadvantages of food competition if the resource is unpredictably patched [57]. Kennedy and Eberhart followed this hypothesis that information exchange between group members offers an evolutionary advantage in the further development of the PSO by implementing social behavior in the birds and, hence, making them mass-and collision-free particles.…”

“…They also extended the model by not only having them look for a rest area but also using a "cornfield vector" on which the best feeding place exists. These particles now have a memory of their best position in relation to such a feeding place and a knowledge of the best position so far within the entire swarm [57]. Based on these considerations, the classic PSO for continuous problems was developed.…”

Optimum design of steel building structures using migration-based vibrating particles system

“…Regarding the fact that hybrid metaheuristic algorithms are presented in this paper for optimum design of building structures, it should be noted that many other hybrid schemes have also been proposed in the literature in which the discrete structural optimization is in perspective. Hybrid plant growth simulation and particle swarm optimization algorithm for structural optimization [29], hybrid Water Cycle and Moth-Flame for solving constrained optimization problems in engineering field [30], Hybrid Harris hawks optimization, slap swarm algorithm, grasshopper optimization and dragonfly algorithm for design optimization of structures [31], hybrid particle swarm and gradient algorithm for structural design optimization [32], Hybrid harmony search, particle swarm and ant colony algorithm for design optimization of structures [33], hybrid Charged System Search-MBLS algorithm for optimum design of truss structures [34], hybrid scheme by implementation of migration and differential evolution strategies in optimization of truss structures [35], hybrid Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) for structural optimization [36], hybrid adaptive meshing strategy (AMS) and bidirectional evolutionary for structural optimization [37], and hybrid evolutionary algorithm for structural optimization [38].…”

A hybrid computational method for optimization design of bistable compliant mechanism

“…Over the past decades, optimization techniques have been proposed, such as GA (Keshtiara et al , 2019), particle swarm optimization (PSO) (Chau et al , 2019), differential evolution (Dao et al , 2017a), cuckoo search (Dao et al , 2017b), INSGA-II (Bu et al , 2019), ANN-based GA (Soepangkat et al , 2019), surrogate-assisted MOO algorithms (Amrit and Leifsson, 2019), improved plant growth simulation algorithm–PSO hybrid algorithm (Jiang et al , 2020) and other algorithms (Chernogorov et al , 2017; Senkerik et al , 2017; Zatloukal and Znoj, 2017; Dinh-Cong et al , 2018). Presently, algorithms with free parameters were developed, e.g.…”