Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

Abstract

Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population's initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for population initialization by incorporating the torus sequence with PSO known as TO-PSO. The torus sequence belongs to the family of low discrepancy sequence and it is utilized in the proposed variant of PSO for the initialization of swarm. The proposed strategy of population's initialization has been observed with the fifteen most famous unimodal and multimodal benchmark test problems. The outcomes of our proposed technique display outstanding performance as compared with the traditional PSO, PSO initialized with Sobol Sequence (SO-PSO) and Halton sequence (HO-PSO). The exhaustive experimental results conclude that the proposed algorithm remarkably superior to the other classical approaches. Additionally, the outcomes produced from our proposed work exhibits anticipation that how immensely the proposed approach highly influences the value of cost function, convergence rate, and diversity.

An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

“…x yij indicates the amount of y material transported from material distribution center i to the relief point j, t yi indicates the unit amount of time used to load y material from material distribution center i, and C ij indicates the cost consumed to transport the unit amount from material distribution center i to the relief point j. Equation (11) indicates that the sum of the storage of y materials at all material distribution centers is not less than the sum of the demand for y materials at all relief points. A yi denotes the storage of y materials at material distribution center i, and B yj denotes the demand for y materials at j relief points.…”

“…However, the basic PSO algorithm has some drawbacks for material scheduling solutions. On the one hand, PSO is initialized with randomly generated population of particles (initial swarm), and the inadequate distribution of the initial swarms in the search region will not only weaken the global optimal solution identification performance when solving in multidimensional space but will also directly impact the algorithm stability [11]. On the other hand, the PSO algorithm is prone to fall into local optima due to constant inertial weights or random changes when dealing with complex problems such as material scheduling [12].…”

Elite-guided multi-objective cuckoo search algorithm based on crossover operation and information enhancement

“…In view of the influence of the initial population diversity on the performance of the algorithm, the latest research proposes that the quasi-random sequence and the ring sequence act on the initial population, and combine them with particle swarm optimization(PSO). The results show that the initial population diversity has a greater impact on the performance of the algorithm (Ashraf et al 2022).…”

An Improved Particle Swarm Optimization Algorithm for Data Classification

“…The PSO algorithm suffers from premature convergence and diversity problems. If PSO parameters are not properly set, then there are chances that it can get trapped in local optimum due to lack of local exploitation, global exploration and diversity issues in the search space [19]. To solve the combinatorial optimization problems, multiple modified PSO variants are proposed in [20,21], such as multi-objective optimization [22], constraint optimization [23], opposition-based variant [24], adaptive inertia weight [25] and mutation operator [26].…”