An Improvement of Database with Local Search Mechanisms for Genetic Algorithms in Large-Scale Computing Environments

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

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Improvement of genetic algorithms with decomposition procedures for large-scale multiobjective multidimensional 0-1 knapsack problems incorporating fuzzy goals

Electronics and Communications in Japan (Part III Fundamental Electronic Science) ◽

10.1002/1520-6440(200012)83:12<62::aid-ecjc7>3.0.co;2-d ◽

2000 ◽

Vol 83 (12) ◽

pp. 62-69 ◽

Cited By ~ 2

Author(s):

Kosuke Kato ◽

Masatoshi Sakawa ◽

Toshinori Ikegame

Keyword(s):

Genetic Algorithms ◽

Large Scale ◽

Knapsack Problems ◽

Fuzzy Goals

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Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

Algorithms ◽

10.3390/a14020045 ◽

2021 ◽

Vol 14 (2) ◽

pp. 45

Author(s):

Rafael D. Tordecilla ◽

Pedro J. Copado-Méndez ◽

Javier Panadero ◽

Carlos L. Quintero-Araujo ◽

Jairo R. Montoya-Torres ◽

...

Keyword(s):

Fuzzy Logic ◽

Local Search ◽

Vehicle Routing Problem ◽

Large Scale ◽

Iterated Local Search ◽

Location Routing Problem ◽

Routing Problem ◽

Location Routing ◽

Fuzzy Pattern ◽

Reasonable Computing

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.

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A Matheuristic Approach Combining Local Search and Mathematical Programming

Scientific Programming ◽

10.1155/2016/1506084 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

Carolina Lagos ◽

Guillermo Guerrero ◽

Enrique Cabrera ◽

Stefanie Niklander ◽

Franklin Johnson ◽

...

Keyword(s):

Mathematical Programming ◽

Local Search ◽

Large Scale ◽

Search Algorithm ◽

Facility Location Problem ◽

Specific Information ◽

Local Search Algorithm ◽

Capacitated Facility Location Problem ◽

Capacitated Facility Location ◽

Installation Cost

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

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