A new Meta heuristic approach called ”Randomized gravitational emulation search algorithm (RGES)” for solving large size set covering problems has been designed. This algorithm is found upon introducing randomization concept along with the two of the four primary parameters ’velocity’ and ’gravity’ in physics. A new heuristic operator is introduced in the domain of RGES to maintain feasibility ...

Meta-heuristic algorithms are well-known optimization methods, for solving real-world problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has slow convergence rate, which causes to have weak exploitation process in finding global optima. Different variants of introduced literature enhance and fix its problems, but most cases, ...

The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvement...

One of the most crucial problems in geo-engineering is the instability of unsaturated slopes, causing severe loss of life and property worldwide. In this study, five novel meta-heuristic methods are employed to optimize locating the Critical Failure Surface (CFS) and corresponding Factor of Safety (FOS). A Finite Element Method (FEM) code is incorporated to convert the strong form of the Richar...

Ant colony optimization (ACO) is a novel and promising meta-heuristic for solving hard combinatorial optimization problems, such as travelling salesman[3] [4] [5] [6] [7] [8], quadratic assignment[9] [10] [11] [12] and set covering problem [13]. Unfortunately, according to systems performance evaluation literature, the methods adopted to experimentally validate the current ACO are not enough ac...

In this research application paper, the usefulness of an intelligent mechanism (a cubic spline smoothing technique) for determining when to switch from one algorithm to another within a meta heuristic search process is explored. We concentrated on a typical planning problem for a southern United States forestry company where the net present value of management activities is maximized subject to...

Meta-heuristics are algorithms which are applied to solve problems when conventional algorithms can not find good solutions in reasonable time; evolutionary algorithms are perhaps the most well-known examples of meta-heuristics. As there are many possible meta-heuristics, finding the most suitable meta-heuristic for a given problem is not a trivial task. In order to make this choice, one can de...

Dispatch optimization of the integrated energy system with multi-type storages, as an essential issue to optimum management, is a high-dimensional complex nonlinear problem time-series constraints. To overcome problem, new meta-heuristic method presented in this paper. The proposed introduces several heuristic rules, called maximum storage utilization and embeds them into adaptive GA improve qu...

This article describes the origin and significant developments associated with the field of meta-heuristics as they relate to global optimization. Meta-heuristics provide a means for approximately solving complex optimization problems. These methods are designed to search for global optima; however, they cannot guarantee that the best solution found after termination criteria are satisfied is i...