Multi-objective programming methodology for solving economic diplomacy resource allocation problem

Abstract

Economic diplomacy is an important prerequisite for achieving the economic goals of any country. The issue is worth analysing from several aspects. Since there is a lack of literature in the field, this paper may be one of the first steps in this direction. It focuses on a clear exposition and explanation of multi-objective programming methodology and its connection with economic diplomacy at the micro-level. This connection is achieved by constructing a model that optimises funds allocation for economic diplomacy costs. The model uses multi-objective programming methodology and takes into account the relevant economic diplomacy funding determinants. It defines measurements of criteria, budget limitation, efficiency maximization, and location. The application of the model is illustrated by a numerical example.

Application of a novel metaheuristic algorithm inspired by stadium spectators in global optimization problems

“…According to the “No Free Lunch” theorem, there is no single algorithm capable of providing efficient solutions for all optimization problems 44 . Therefore, investigating and modifying existing algorithms for solving optimization problems is a highly-active research field since more information is needed on the efficiency of metaheuristic algorithms and their capability of providing good solutions to various real-life optimization problems 45 – 47 .…”

A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves

“…These range from traditional optimization techniques that use both linear and nonlinear programming [6], to the newer nature-inspired meta-heuristics [7,8], each with their own strengths and weaknesses. Despite being successful in solving well-known optimization problems [2,9], traditional algorithms on one side suffer from inherent dependency on gradient information and the desperate need for a promising initial starting vector within the search space [2,9]. The existing nature-inspired metaheuristic optimizers, on the other side, are highly problem dependent in that they might be very successful in solving certain problems and may not be able to provide satisfactory solutions for other problems [10].…”