Bio-Inspired Metaheuristics

2020 ◽  
Vol 10 (4) ◽  
pp. 1-18
Author(s):  
Rachid Kaleche ◽  
Zakaria Bendaoud ◽  
Karim Bouamrane
Keyword(s):  
Real Life ◽  
Optimal Solution ◽  
Np Hard ◽  
Exact Methods ◽  
Approximate Methods ◽  
Life Problems ◽  
Comprehensive Survey ◽  
Hard Problems ◽  
Exact And Approximate Methods ◽  
Np Hard Problems

In real life, problems becoming more complicated, among them NP-Hard problems. To resolve them, two families of methods exist, exact and approximate methods. When exact methods provide the optimal solution in an unacceptable amount of time, the approximate ones provide good solutions in a reasonable amount of time. Approximate methods are two kinds, heuristics and metaheuristics. The first ones are problem specific, while metaheuristics are independent from problems. A broad number of metaheuristics are inspired from nature, specially from biology. These bio-inspired metaheuristics are easy to implement and provide interesting results. This paper aims to provide a comprehensive survey of bio-inspired metaheuristics, their classification, principals, algorithms, their application domains, and a comparison between them.

scholarly journals Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

2021 ◽  
Author(s):  
Saman Almufti
Keyword(s):  
Real Life ◽  
Optimal Solution ◽  
Population Based ◽  
Performance Evaluations ◽  
Np Hard ◽  
Life Problems ◽  
Hard Problems ◽  
Optimum Solution ◽  
Over Time ◽  
Np Hard Problems

Metaheuristics is one of the most well-known field of researches uses to find optimum solution for Non-deterministic polynomial hard problems (NP-Hard), that are difficult to find an optimal solution in a polynomial time. Over time many algorithms have been developed based on the heuristics to solve difficult real-life problems, this paper will introduce Metaheuristic-based algorithms and its classifications, Non-deterministic polynomial hard problems. It also will compare the performance two metaheuristic-based algorithms (Elephant Herding optimization algorithm and Tabu Search) to solve Traveling Salesman Problem (TSP), which is one of the most known problem belongs to Non-deterministic polynomial hard problem and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. the experimental results of the paper compare the results of EHO and TS for solving 10 different problems from the TSPLIB95.

scholarly journals A Parallel Algorithm for Matheuristics: A Comparison of Optimization Solvers

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1541
Author(s):  
Martín González ◽  
Jose J. López-Espín ◽  
Juan Aparicio
Keyword(s):  
Parallel Algorithm ◽  
Shared Memory ◽  
Mixed Integer ◽  
Np Hard ◽  
Exact Methods ◽  
Optimization Software ◽  
Software Packages ◽  
Metaheuristic Methods ◽  
Hard Problems ◽  
Np Hard Problems

Metaheuristic and exact methods are one of the most common tools to solve Mixed-Integer Optimization Problems (MIPs). Most of these problems are NP-hard problems, being intractable to obtain optimal solutions in a reasonable time when the size of the problem is huge. In this paper, a hybrid parallel optimization algorithm for matheuristics is studied. In this algorithm, exact and metaheuristic methods work together to solve a Mixed Integer Linear Programming (MILP) problem which is divided into two different subproblems, one of which is linear (and easier to solve by exact methods) and the other discrete (and is solved using metaheuristic methods). Even so, solving this problem has a high computational cost. The algorithm proposed follows an efficient decomposition which is based on the nature of the decision variables (continuous versus discrete). Because of the high cost of the algorithm, as this kind of problem belongs to NP-hard problems, parallelism techniques have been incorporated at different levels to reduce the computing cost. The matheuristic has been optimized both at the level of the problem division and internally. This configuration offers the opportunity to improve the computational time and the fitness function. The paper also focuses on the performance of different optimization software packages working in parallel. In particular, a comparison of two well-known optimization software packages (CPLEX and GUROBI) is performed when they work executing several simultaneous instances, solving various problems at the same time. Thus, this paper proposes and studies a two-level parallel algorithm based on message-passing (MPI) and shared memory (Open MP) schemes where the two subproblems are considered and where the linear problem is solved by using and studying optimization software packages (CPLEX and GUROBI). Experiments have also been carried out to ascertain the performance of the application using different programming paradigms (shared memory and distributed memory).

NP vs QMA_\log(2)

2010 ◽  
Vol 10 (1&2) ◽  
pp. 141-151
Author(s):  
S. Beigi
Keyword(s):  
Quantum Algorithms ◽  
Np Hard ◽  
Complete Problems ◽  
Hard Problems ◽  
Np Complete ◽  
Np Hard Problems

Although it is believed unlikely that $\NP$-hard problems admit efficient quantum algorithms, it has been shown that a quantum verifier can solve NP-complete problems given a "short" quantum proof; more precisely, NP\subseteq QMA_{\log}(2) where QMA_{\log}(2) denotes the class of quantum Merlin-Arthur games in which there are two unentangled provers who send two logarithmic size quantum witnesses to the verifier. The inclusion NP\subseteq QMA_{\log}(2) has been proved by Blier and Tapp by stating a quantum Merlin-Arthur protocol for 3-coloring with perfect completeness and gap 1/24n^6. Moreover, Aaronson et al. have shown the above inclusion with a constant gap by considering $\widetilde{O}(\sqrt{n})$ witnesses of logarithmic size. However, we still do not know if QMA_{\log}(2) with a constant gap contains NP. In this paper, we show that 3-SAT admits a QMA_{\log}(2) protocol with the gap 1/n^{3+\epsilon}} for every constant \epsilon>0.

scholarly journals Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems

2020 ◽  
Vol 10 (6) ◽  
pp. 2075 ◽  
Author(s):  
Shih-Cheng Horng ◽  
Shieh-Shing Lin
Keyword(s):  
Optimization Problems ◽  
Solution Space ◽  
Inequality Constraints ◽  
Optimization Methods ◽  
Ordinal Optimization ◽  
Np Hard ◽  
Constrained Optimization Problems ◽  
Inequality Constrained Optimization ◽  
Hard Problems ◽  
Np Hard Problems

The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find N significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the N significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods.

Social Insect Societies for the Optimization of Dynamic NP-Hard Problems

2011 ◽  
pp. 43-67 ◽  
Author(s):  
Stephan Hartmann ◽  
Pedro Pinto ◽  
Thomas Runkler ◽  
João Sousa
Keyword(s):  
Social Insect ◽  
Np Hard ◽  
Insect Societies ◽  
Hard Problems ◽  
Np Hard Problems

scholarly journals The effect of the application of the exact and approximate methods on values of selected ecological indices

2014 ◽  
Vol 67 (1) ◽  
pp. 39-46
Author(s):  
Maria Ługowska ◽  
Zofia Rzymowska
Keyword(s):  
Wiener Index ◽  
Ecological Indices ◽  
Exact Methods ◽  
Approximate Methods ◽  
Winter Cereals ◽  
Spring Cereals ◽  
Phytosociological Relevés ◽  
Exact And Approximate Methods ◽  
Potato Crops ◽  
Tuber Crops

<p>The work presents the results of a study on the biodiversity of agrocenoses using ecological indices. In order to calculate the measures, phytosociological relevés were made and exact methods were applied in winter cereals, spring cereals, tuber crops and stubble fields. The objective of the work was to compare ecological indices (Simpson’s index of dominance <em>C</em>, Simpson’s index of species richness <em>D,</em> and Shannon-Wiener index of biodiversity <em>H</em>’) calculated using the number of plants and their cover determined based on the degree of presence. Moreover, correlation analysis was conducted between the indices computed using the two approaches applied.</p><p>The results of the study revealed significant differences between all the indices calculated using the exact and approximate methods. In turn, comparisons of the measures computed for individual crops showed significant differences only for potato crops and winter cereals. No significant differences were found between the indicators calculated for spring cereals and stubble fields.</p>

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