In many meta-heuristics greedy choices are performed and usually measured by means of an immediate gain eventually leading to suboptimal choices. Usually, the measure of this gain as well as a mechanism to guide the choices given the evaluations is performed rather shortsighted. Therefore, especially in the artificial intelligence community researchers have thought about looking ahead (see, e.g...

This paper presents a scatter search (SS) based method for the bi-criteria multi-dimensional knapsack problem. The method is organized according to the usual structure of SS: 1) diversi...cation 2) improvement 3) reference set update 4) subset generation, and 5) solution combination. Surrogate relaxation is used to convert the multi-constraint problem into a single constraint one, which is used...

Hybrids of meta-heuristics have been shown to be more effective and adaptable than their parents in solving various combinatorial optimization problems. However, hybridized schemes are more tedious to implement due to their complexity. We address this problem by proposing the Meta-heuristics Development Framework (MDF). In addition to being a framework that promotes reuse to reduce developmenta...

In this research, an integrated inventory problem is formulated for a single-vendor multiple-retailer supply chain that works according to the vendor managed inventory policy. The model is derived based on the economic order quantity in which shortages with penalty costs at the retailers` level is permitted. As predicting customer demand is the most important problem in inventory systems and th...

Hyper-heuristics can be thought of as heuristic management methodologies which are motivated by the goal of building a high level of generality in the scope of computational search methodologies [2] [7]. Most meta-heuristics (and other search methods) perform a search over the solution space directly, whereas, hyper-heuristics as high level strategies search a heuristic space. One of the hyper-...

Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of facilities conditions profits, facility problem (CFLP) has been extensively applied literature. This generally falls within class NP-hard problems, which are difficult solve. Therefore, choosing a solution method optimize is key factor. Even though CFLPs have con...

Heterogeneous computing HC environments are well suited to meet the computational demands of large diverse groups of tasks i e a meta task The prob lem of mapping de ned as matching and scheduling these tasks onto the machines of an HC environment has been shown in general to be NP complete requir ing the development of heuristic techniques Selecting the best heuristic to use in a given environ...

This paper presents the first investigation on applying a Particle Swarm Optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics...

Nurse rostering problems consist of assigning varying tasks, represented as shift types, to hospital personnel with different skills and work regulations. We want to satisfy as many constraints and personal preferences as possible while constructing a schedule which meets the personnel requirements of the hospital over a predefined planning period. Real world situations are often so constrained...

Abstract To develop new meta-heuristic algorithms and evaluate on the benchmark functions is most challenging task. In this paper, performance of various developed are evaluated recently CEC 2021 functions. The objective parametrized by inclusion operators, such as bias, shift rotation. different combinations binary operators applied to which leads CEC2021 Therefore, considered solve with dimen...