## 1 Introduction
Their implementation was performed using the GA toolbox of MATLAB program. Then a hybrid procedure based on GAs (HGA) was developed by [PERSON] et al. [9] to minimize the instrumentation network cost subject to precision constraints on key variables. They used a structured population in the form of neighbourhoods and a local optimizer of the best current solutions, which provide a good balance between the algorithm capabilities of exploration and exploitation. Recently, [PERSON] and [PERSON] [10] proposed a general approach for designing the cheapest SN able to detect and locate a set of specified faults. They applied the previously developed parallel procedure [7] to select sensors and their locations. In recent years some applications of Tabu Search (TS) meta-heuristic [11] for the solution of chemical engineering problems have appeared [12 - 14]. The technique guides a local-search procedure that explores the solution space beyond local optimality. The local search uses an operation called move to define the neighbourhood of a given solution. It was reported that TS has a more flexible and effective search behaviour than other stochastic methods as consequence of the use of adaptive memory. This motivated the development of new strategies for the design and upgrade of SNs. Within the framework of TS, a Strategic Oscillation Technique around the feasibility boundary (SO-TS) was reported by [PERSON] et al. [15-16]. A comparative performance-analysis indicated that the strategy efficiently searches the solution space, significantly reducing the number of required calls to the evaluation function in comparison with HGA and the Classic TS (C-TS) [9]. There exist other population-based methodologies that have demonstrated a rewarding performance for solving hard optimization problems and constitute attractive alternatives to GAs. In this regard, Scatter Search (SS) [17-18] is founded on the premise that systematic designs and methods for creating new solutions afford significant benefits beyond those derived from recourse to randomization. Also Estimation of Distribution Algorithms (EDAs) [19-20] offer a novel evolutionary paradigm. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. Within the framework of EDAs approach, Population Based Incremental Learning Algorithms (PBIL) are devised, which introduce the concepts of competitive learning (typical in artificial neural networks) to direct the search [21]. Successful applications of SS and PBIL have been reported to solve complex combinatorial problems, such as the vehicle routing, knapsack and scheduling problems [22 - 24]. Furthermore Path Relinking (PR) has been proposed as a method to better explore the solution space of complex problems when a set of promising solutions is known [11]. It has been incorporated to algorithms based on TS to provide better intensification and diversification capabilities to the search. The technique is recognized as an effective tool to solve difficult combinatorial problems [25]. The rewarding performance of SS and PBIL algorithms for solving multimodal optimization problems with a huge number of binary or integer variables sustains their selection to address the SNDP and perform a comparative performance analysis among SO-TS, SS and PBIL. Furthermore the performance of the effective search strategy PR is analyzed within the framework of TS and compared also with SO-TS. For this work new strategies to solve the sensor location problem are developed. They are based on the meta-heuristics SS, PBIL and PR within the framework of TS (PR-TS). The distinctive features of the algorithms are provided and comparative performance studies are conducted among SO-TS, PR-TS, SS and PBIL for solving instrumentation designs of industrial networks extracted from the literature. The rest of the paper is organized as follows. In Section 2 the design problem is briefly introduced. Sections 3 to 5 present algorithms based on the meta-heuristics TS, SS and PBIL respectively.