Application of Salp Swarm Algorithm to Solve Constrained Optimization Problems with Dynamic Penalty Approach in Real-Life Problems

2021 ◽  
pp. 73-82
Author(s):  
Omkar Kulkarni ◽  
G. M. Kakandikar ◽  
V. M. Nandedkar
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Real Life ◽  
Constrained Optimization Problems ◽  
Salp Swarm Algorithm ◽  
Penalty Approach ◽  
Life Problems ◽  
Swarm Algorithm

scholarly journals Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Minggang Dong ◽  
Ning Wang ◽  
Xiaohui Cheng ◽  
Chuanxian Jiang
Keyword(s):  
Differential Evolution ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Real Life ◽  
Optimization Criterion ◽  
Constrained Optimization Problems ◽  
Constraints Handling ◽  
Efficient Alternative ◽  
Handling Methods

Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE) for constrained optimization problems (COPs). More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.

Particle Swarm Algorithm Based on Boundary Buffering-Natural Evolution and its Application in Constrained Optimization

2014 ◽  
Vol 670-671 ◽  
pp. 1517-1521
Author(s):  
Tie Bin Wu ◽  
Tao Yun Zhou ◽  
Wen Li ◽  
Gao Feng Zhu ◽  
Yun Lian Liu
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Local Optimum ◽  
Natural Evolution ◽  
Constrained Optimization Problems ◽  
Simulation Results ◽  
Swarm Algorithm ◽  
Mutation Strategies

A particle swarm algorithm (PSO) based on boundary buffering-natural evolution was proposed for solving constrained optimization problems. By buffering the particles that cross boundaries, the diversity of populations was intensified; to accelerate the convergence speed and avoid local optimum of PSO, natural evolution was introduced. In other words, particle hybridization and mutation strategies were applied; and by combining the modified feasible rules, the constrained optimization problems were solved. The simulation results proved that the method was effective in solving this kind of problems.

Advancement of the search process of salp swarm algorithm for global optimization problems

2021 ◽  
pp. 115292
Author(s):  
Emre Çelik ◽  
Nihat Öztürk ◽  
Yogendra Arya
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Process ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm
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