scholarly journals Controlling of Local search Methods’ Parameters in Memetic Algorithms Using the Principles of Simulated Annealing

2016 ◽  
Vol 136 ◽  
pp. 70-76 ◽  
Author(s):  
Peter Pecháč ◽  
Milan Sága
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Memetic Algorithms ◽  
Search Methods ◽  
Local Search Methods

scholarly journals One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3030
Author(s):  
Raúl Mencía ◽  
Carlos Mencía
Keyword(s):  
Local Search ◽  
Electric Vehicles ◽  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Hill Climbing ◽  
Search Methods ◽  
Local Search Methods ◽  
Local Search Procedure ◽  
One Machine Scheduling ◽  
One Machine

This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches.

Memetic Algorithms for Continuous Optimisation Based on Local Search Chains

2010 ◽  
Vol 18 (1) ◽  
pp. 27-63 ◽  
Author(s):  
Daniel Molina ◽  
Manuel Lozano ◽  
Carlos García-Martínez ◽  
Francisco Herrera
Keyword(s):  
Local Search ◽  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Internal Variables ◽  
Search Methods ◽  
Local Tuning ◽  
Local Search Methods ◽  
Optimisation Methods ◽  
Parameter Values ◽  
Local Search Operator

Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, on some occasions, they may become very expensive, because of the way they exploit local information to guide the search process. In this paper, they are called intensive continuous local search methods. Given the potential of this type of local optimisation methods, it is interesting to build prospective memetic algorithm models with them. This paper presents the concept of local search chain as a springboard to design memetic algorithm approaches that can effectively use intense continuous local search methods as local search operators. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration (initial solution, strategy parameter values, internal variables, etc.) reached by this one. The proposed memetic algorithm favours the formation of local search chains during the memetic algorithm run with the aim of concentrating local tuning in search regions showing promise. In order to study the performance of the new memetic algorithm model, an instance is implemented with CMA-ES as an intense local search method. The benefits of the proposal in comparison to other kinds of memetic algorithms and evolutionary algorithms proposed in the literature to deal with continuous optimisation problems are experimentally shown. Concretely, the empirical study reveals a clear superiority when tackling high-dimensional problems.

scholarly journals BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

2020 ◽  
Author(s):  
Kushal Kanti Ghosh ◽  
Ritam Guha ◽  
Suman Kumar Bera ◽  
Ram Sarkar ◽  
Seyedali Mirjalili
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Transfer Function ◽  
Local Search ◽  
Search Methods ◽  
Selection Problems ◽  
Local Search Methods ◽  
Wide Range ◽  
Binary Problems

Abstract This work proposed a binary variant of the recently-proposed Equilibrium Optimizer (EO) to solve binary problems. A v-shaped transfer function is used to map continuous values created in EO to binary. To improve the exploitation of the Binary Equilibrium Optimizer (BEO), the Simulated Annealing is used as one of the most popular local search methods. The proposed BEO algorithm is applied to 18 UCI datasets and compared to a wide range of algorithms. The results demonstrate the superiority and merits of EO when solving feature selection problems.

scholarly journals Planning capacity for 5G and beyond wireless networks by discrete fireworks algorithm with ensemble of local search methods

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Hafiz Munsub Ali ◽  
Jiangchuan Liu ◽  
Waleed Ejaz
Keyword(s):  
Wireless Networks ◽  
Local Search ◽  
Wireless Network ◽  
Statistical Tests ◽  
Mathematical Framework ◽  
Integer Programming Problem ◽  
Search Methods ◽  
Fireworks Algorithm ◽  
Local Search Methods ◽  
Planning Capacity

Abstract In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

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