Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints

Abstract

Heuristic algorithms have shown a good ability to solve a variety of optimization problems. Stockpile blending problem as an important component of the mine scheduling problem is an optimization problem with continuous search space containing uncertainty in the geologic input data. The objective of the optimization process is to maximize the total volume of materials of the operation and subject to resource capacities, chemical processes, and customer requirements. In this paper, we consider the uncertainty in material grades and introduce chance constraints that are used to ensure the constraints with high confidence. To address the stockpile blending problem with chance constraints, we propose a differential evolution algorithm combining two repair operators that are used to tackle the two complex constraints. In the experiment section, we compare the performance of the approach with the deterministic model and stochastic models by considering different chance constraints and evaluate the effectiveness of different chance constraints.

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

“…Although this boundary limiting method works efficiently for linear and convex problems, it leads the algorithm toward stagnation in the case of multi-objective nonlinear functions such as the OPF problem. Hence, to avoid stagnation, a randomized-based variable boundary limiting is introduced in the proposed WMFO based on Equation (32), where x id denotes the value of d-th dimension of i-th search agent, and r is a random value between intervals 0 and 1.…”

“…Metaheuristic algorithms are a subset of stochastic algorithms that have been employed for solving complex problems such as feature selection [8][9][10][11][12], engineering [13][14][15][16][17][18][19][20][21][22][23][24][25][26], community detection [27][28][29][30], and continuous optimization [31][32][33][34][35][36][37] problems. Metaheuristic algorithms employ stochastic techniques to discover the promising areas by exploring the search space in early iterations and improve solutions quality by exploiting the promising areas in the final iterations.…”