Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization

2020 ◽  
Vol 12 (2) ◽  
pp. 74-86 ◽  
Author(s):  
Kenneth Brezinski ◽  
Michael Guevarra ◽  
Ken Ferens
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Population Based ◽  
Convergence Time ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Benchmark Instances

This article introduces a hybrid algorithm combining simulated annealing (SA) and particle swarm optimization (PSO) to improve the convergence time of a series of combinatorial optimization problems. The implementation carried out a dynamic determination of the equilibrium loops in SA through a simple, yet effective determination based on the recent performance of the swarm members. In particular, the authors demonstrated that strong improvements in convergence time followed from a marginal decrease in global search efficiency compared to that of SA alone, for several benchmark instances of the traveling salesperson problem (TSP). Following testing on 4 additional city list TSP problems, a 30% decrease in convergence time was achieved. All in all, the hybrid implementation minimized the reliance on parameter tuning of SA, leading to significant improvements to convergence time compared to those obtained with SA alone for the 15 benchmark problems tested.

scholarly journals On the Binarization of Grey Wolf Optimizer‎: ‎A Novel Binary Optimizer Algorithm

2021 ◽  
Author(s):  
Mehdy Roayaei
Keyword(s):  
Combinatorial Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Combinatorial Optimization Problems ◽  
Hunting Behaviour ◽  
Set Operations

Abstract ‎Grey Wolf Optimizer (GWO) is a population-based evolutionary algorithm inspired by the hunting behaviour of grey wolves‎. ‎GWO‎, ‎in its basic form‎, ‎is a real coded algorithm‎, ‎therefore‎, ‎it needs modifications to deal with binary optimization problems‎. ‎In this paper‎, ‎we review previous works on binarization of GWO‎, ‎and classify them with respect to their encoding scheme‎, ‎updating strategy‎, ‎and transfer function‎. ‎Then‎, ‎we propose a novel binary GWO algorithm (named SetGWO)‎, ‎which is based on set encoding and uses set operations in its updating strategy‎. ‎Experimental results on different real-world combinatorial optimization problems and different datasets‎, ‎show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions‎, ‎running time‎, ‎and scalability‎.

scholarly journals Solving the Quadratic Assignment Problem using the Swallow Swarm Optimization Problem

2019 ◽  
Vol 8 (6) ◽  
pp. 3116-3120
Keyword(s):  
Assignment Problem ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Combinatorial Problems ◽  
Computational Method ◽  
Quadratic Assignment ◽  
Swarm Optimization ◽  
Natural Behavior ◽  
Combinatorial Optimization Problems ◽  
Benchmark Instances

In recent years, there is a growing interest in swarm intelligent algorithms inspired by the observation of the natural behavior of swarm to define a computational method, which may resolve the hardest combinatorial optimization problems. The Quadratic Assignment Problem is one of the well-known combinatorial problems, which simulate with the assignment problem in several domains such as the industrial domain. This paper proposes an adaptation of a recent algorithm called the swallow swarm optimization to solve the Quadratic Assignment Problem; this algorithm is characterized by a hierarchy of search who allow it to search in a totality of research space. The obtained results in solving some benchmark instances from QAPLIB are compared with those obtained from other know metaheuristics in other to evaluate the performance of the proposed adaptation.

Particle Swarm Optimization from Theory to Applications

2018 ◽  
Vol 5 (2) ◽  
pp. 1-24 ◽  
Author(s):  
M.A. El-Shorbagy ◽  
Aboul Ella Hassanien
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Real Life ◽  
Particle Swarm ◽  
Population Based ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Unconstrained Optimization Problems

Particle swarm optimization (PSO) is considered one of the most important methods in swarm intelligence. PSO is related to the study of swarms; where it is a simulation of bird flocks. It can be used to solve a wide variety of optimization problems such as unconstrained optimization problems, constrained optimization problems, nonlinear programming, multi-objective optimization, stochastic programming and combinatorial optimization problems. PSO has been presented in the literature and applied successfully in real life applications. In this paper, a comprehensive review of PSO as a well-known population-based optimization technique. The review starts by a brief introduction to the behavior of the PSO, then basic concepts and development of PSO are discussed, it's followed by the discussion of PSO inertia weight and constriction factor as well as issues related to parameter setting, selection and tuning, dynamic environments, and hybridization. Also, we introduced the other representation, convergence properties and the applications of PSO. Finally, conclusions and discussion are presented. Limitations to be addressed and the directions of research in the future are identified, and an extensive bibliography is also included.

scholarly journals Distance Evaluated Simulated Kalman Filter with State Encoding for Combinatorial Optimization Problems

2018 ◽  
Vol 7 (4.27) ◽  
pp. 22
Author(s):  
Zulkifli Md Yusof ◽  
Zuwairie Ibrahim ◽  
Asrul Adam ◽  
Kamil Zakwan Mohd Azmi ◽  
Tasiransurini Ab Rahman ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Combinatorial Problem ◽  
Search Space ◽  
Population Based ◽  
Routing And Scheduling ◽  
Combinatorial Optimization Problems ◽  
Binary Encoding ◽  
State Encoding

Simulated Kalman Filter (SKF) is a population-based optimization algorithm which exploits the estimation capability of Kalman filter to search for a solution in a continuous search space. The SKF algorithm only capable to solve numerical optimization problems which involve continuous search space. Some problems, such as routing and scheduling, involve binary or discrete search space. At present, there are three modifications to the original SKF algorithm in solving combinatorial optimization problems. Those modified algorithms are binary SKF (BSKF), angle modulated SKF (AMSKF), and distance evaluated SKF (DESKF). These three combinatorial SKF algorithms use binary encoding to represent the solution to a combinatorial optimization problem. This paper introduces the latest version of distance evaluated SKF which uses state encoding, instead of binary encoding, to represent the solution to a combinatorial problem. The algorithm proposed in this paper is called state-encoded distance evaluated SKF (SEDESKF) algorithm. Since the original SKF algorithm tends to converge prematurely, the distance is handled differently in this study. To control and exploration and exploitation of the SEDESKF algorithm, the distance is normalized. The performance of the SEDESKF algorithm is compared against the existing combinatorial SKF algorithm based on a set of Traveling Salesman Problem (TSP).      

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