Simple probabilistic population based optimization for combinatorial optimization

Real World ◽

Optimization Problems ◽

Population Based ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

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.

Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.2020040105 ◽

2020 ◽

Vol 12 (2) ◽

pp. 74-86 ◽

Cited By ~ 3

Author(s):

Kenneth Brezinski ◽

Michael Guevarra ◽

Ken Ferens

Keyword(s):

Optimization Problems ◽

Parameter Tuning ◽

Population Based ◽

Convergence Time ◽

Benchmark Problems ◽

Swarm Optimization ◽

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.

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

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.27.22431 ◽

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 ◽

Optimization Problems ◽

Combinatorial Problem ◽

Search Space ◽

Population Based ◽

Routing And Scheduling ◽

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).

SOLVING A SINGLE MACHINE SCHEDULING PROBLEM BY A DISCRETE VERSION OF ELECTROMAGNETISM-LIKE METHOD

Journal of Circuits System and Computers ◽

10.1142/s0218126609005885 ◽

2009 ◽

Vol 18 (08) ◽

pp. 1597-1608 ◽

Cited By ~ 4

Author(s):

NIKBAKHSH JAVADIAN ◽

MOHSEN GOLALIKHANI ◽

REZA TAVAKKOLI-MOGHADDAM

Keyword(s):

Combinatorial Optimization Problems

Single Machine ◽

Optimization Problems ◽

Continuous Optimization ◽

Machine Scheduling ◽

Population Based ◽

Single Machine Scheduling ◽

Discrete Version ◽

Scheduling Problem ◽

The electromagnetism-like method (EM) is a population based meta-heuristic algorithm utilizing an attraction-repulsion mechanism to move sample points (i.e., our solutions) towards the optimality. In general, the EM has been initially used for solving continuous optimization problems and could not be applied on combinatorial optimization ones. This paper proposes a discrete binary version of the EM for solving combinatorial optimization problems. To show the efficiency of our proposed EM, we solve a single machine scheduling problem and compare our computational results with the solutions reported in the literature. Finally, we conclude that our proposed method is capable of solving such well-known problems more efficiently than the previous studies.

Evolutionary and population-based methods versus constructive search strategies in dynamic combinatorial optimization

Information Sciences ◽

10.1016/j.ins.2017.08.058 ◽

2017 ◽

Vol 420 ◽

pp. 159-183 ◽

Cited By ~ 16

Author(s):

Adil Baykasoğlu ◽

Fehmi B. Ozsoydan

Keyword(s):

Population Based ◽

Search Strategies

Implementacija algoritma kolonije mrava za optimalnu rekonfiguraciju distributivne mreže

Energija, Ekonomija, Ekologija ◽

10.46793/eee20-1-2.050m ◽

2020 ◽

Vol 22 (1-2) ◽

pp. 50-57

Author(s):

◽

Nikola Rajaković

Keyword(s):

Distribution Systems ◽

Heuristic Algorithms ◽

Optimization Problems ◽

Population Based ◽

Active Power ◽

Search Technique ◽

Ant Colonies ◽

Heuristic Technique

The paper analyzes the possibility of application of Ant Colony Optimization (ACO) algorithm for reconfiguration of distribution network with the aim of active power minimiza- tion. ACO is a population-based meta-heuristic technique used to solve different combinatorial optimization problems. The search technique is inspired by the behaviour of ant colonies in nature. The efficiency of the proposed algorithm is demonstrated on IEEE 33-bus and IEEE 69-bus test distribution systems. Also, the results obtained by using ACO algorithm are compared to the results achievable by other heuristic and meta-heuristic algorithms.