scholarly journals Hybrid PSOGSA technique for solving dynamic economic emission dispatch problem

2020 ◽  
Vol 40 (3) ◽  
pp. 96-104
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Test System ◽  
Hybrid Population ◽  
Swarm Optimization ◽  
Emission Dispatch ◽  
Population Based Algorithm ◽  
Economic Emission Dispatch

In this paper, a new hybrid population-based algorithm is proposed with the combining of particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques. The main idea is to integrate the ability of exploration in PSO with the ability of exploration in the GSA to synthesize both algorithms’ strength. The new algorithm is implemented to the dynamic economic emission dispatch (DEED) problem to minimize both fuel cost and emission simultaneously under a set of constraints. To demonstrate the efficiency of the proposed algorithm, a 5-unit test system is used. The results show the effectiveness and superiority of the proposed method when compared to the results of other optimization algorithms reported in the literature.

scholarly journals A Memetic Algorithm for the Green Vehicle Routing Problem

2019 ◽  
Vol 11 (21) ◽  
pp. 6055 ◽  
Author(s):  
Bo Peng ◽  
Yuan Zhang ◽  
Yuvraj Gajpal ◽  
Xiding Chen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Population Based ◽  
Longest Common Subsequence ◽  
Routing Problem ◽  
Highly Effective ◽  
Local Search Procedure ◽  
Common Subsequence ◽  
Population Based Algorithm

The green vehicle routing problem is a variation of the classic vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need of recharge during their duties. As an NP-hard problem, this problem is very difficult to solve. In this paper, we first propose a memetic algorithm (MA)—a population-based algorithm—to tackle this problem. To be more specific, we incorporate an adaptive local search procedure based on a reward and punishment mechanism inspired by reinforcement learning to effectively manage the multiple neighborhood moves and guide the search, an effective backbone-based crossover operator to generate the feasible child solutions to obtain a better trade-off between intensification and diversification of the search, and a longest common subsequence-based population updating strategy to effectively manage the population. The purpose of this research is to propose a highly effective heuristic for solving the green vehicle routing problem and bring new ideas for this type of problem. Experimental results show that our algorithm is highly effective in comparison with the current state-of-the-art algorithms. In particular, our algorithm is able to find the best solutions for 84 out of the 92 instances. Key component of the approach is analyzed to evaluate its impact on the proposed algorithm and to identify the appropriate search mechanism for this type of problem.

Birds foraging search: a novel population-based algorithm for global optimization

2019 ◽  
Vol 11 (3) ◽  
pp. 221-250 ◽  
Author(s):  
Zhuoran Zhang ◽  
Changqiang Huang ◽  
Kangsheng Dong ◽  
Hanqiao Huang
Keyword(s):  
Global Optimization ◽  
Population Based ◽  
Population Based Algorithm
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