scholarly journals Three New Stochastic Local Search Metaheuristics for the Annual Crop Planning Problem Based on a New Irrigation Scheme

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Sivashan Chetty ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Local Search ◽  
Agricultural Land ◽  
Absolute Difference ◽  
Planning Problem ◽  
Stochastic Local Search ◽  
Irrigation Scheme ◽  
Crop Planning ◽  
Annual Crop ◽  
Agricultural Planning ◽  
Metaheuristic Techniques

Annual Crop Planning (ACP) is an NP-hard-type optimization problem in agricultural planning. It involves finding optimal solutions concerning the seasonal allocations of a limited amount of agricultural land amongst the various competing crops that are required to be grown on it. This study investigates the effectiveness of employing three new local search (LS) metaheuristic techniques in determining solutions to an ACP problem at a new Irrigation Scheme. These three new LS metaheuristic techniques are the Best Performance Algorithm (BPA), Iterative Best Performance Algorithm (IBPA), and the Largest Absolute Difference Algorithm (LADA). The solutions determined by these LS metaheuristic techniques are compared against the solutions of two other well-known LS metaheuristic techniques in the literature. These techniques are Tabu Search (TS) and Simulated Annealing (SA). The comparison with TS and SA was to determine the relative merits of the solutions found by BPA, IBPA, and LADA. The results show that TS performed as the overall best. However, LADA determined the best solution that was the most economically feasible.

scholarly journals Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors

PLoS ONE ◽  
2017 ◽  
Vol 12 (8) ◽  
pp. e0180813 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Sivashan Chetty
Keyword(s):  
Planning Problem ◽  
Economic Factors ◽  
Crop Planning ◽  
Annual Crop

Stochastic local search with learning automaton for the swap-body vehicle routing problem

2018 ◽  
Vol 89 ◽  
pp. 68-81 ◽  
Author(s):  
Túlio A.M. Toffolo ◽  
Jan Christiaens ◽  
Sam Van Malderen ◽  
Tony Wauters ◽  
Greet Vanden Berghe
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Stochastic Local Search ◽  
Routing Problem ◽  
Learning Automaton

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

An FPGA Solver for Partial MaxSAT Problems Based on Stochastic Local Search

2017 ◽  
Vol 44 (4) ◽  
pp. 32-37
Author(s):  
Shohei Sassa ◽  
Kenji Kanazawa ◽  
Shaowei Cai ◽  
Moritoshi Yasunaga
Keyword(s):  
Local Search ◽  
Stochastic Local Search
Sign in / Sign up

Export Citation Format

Share Document