The Strategic Design of the eBPA

Theoretical Analysis of the enhanced Best Performance Algorithm

2.4 The Strategic Design of the eBPA

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strategy is additionally used to tweak the rate of exploitation, and eliminates the possibilities of cycling for 𝑃𝐿 sizes greater than one.

32 2.4.1 Memory Technique of the eBPA

The fitness value of a solution registered in the memory structure refers to its strength upon having been evaluated by objective function 𝑓. Solutions with better fitness values exist closer to the global optimum point. The qualities of the fitness values are important in differentiating one solution from the next. Importantly, in using the fitness values, the 𝑏𝑒𝑠𝑡 and 𝑤𝑜𝑟𝑠𝑡 solutions can be identified and indexed. The index of the 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 solution will always be that of the most recent solution inserted into the memory structure. The maintenance of these indices are critical in implementing the search strategies of the eBPA. These indices relate to the way the memory structure will adapt as the search progresses. The enforced restrictions of the admittance criterion, coupled with the maintenance of these indices, is core to the design of the eBPA.

The admittance criterion directly influences the trajectory path of the search, as this feature controls the quality of the solutions registered in the memory structure. If 𝑤𝑜𝑟𝑘𝑖𝑛𝑔^′ is allowed admittance, and has a higher fitness value compared to that of 𝑤𝑜𝑟𝑘𝑖𝑛𝑔, then a more attractive position within the solution space has been identified; this implicitly could also refer to the best solution found. If 𝑤𝑜𝑟𝑘𝑖𝑛𝑔^′ has a lower fitness value (i.e. a dis-improved solution) compared to that of 𝑤𝑜𝑟𝑘𝑖𝑛𝑔, and has been allowed admittance, then a less attractive but acceptable position has been identified in the trajectory of the search. This strategy could possibly lead to an alternative route in locating the local optimum point; it could also cause a redirection to other neighboring regions in escaping from premature convergence.

As the search matures, the quality of the solutions in the memory structure are increasingly refined as higher quality solutions get accepted. With improved solutions, the admittance criterion would become increasingly restrictive. The increase in the restrictiveness of the admittance criterion controls the trading-off between exploration and exploitation.

2.4.2 Search Strategy of the eBPA

The search techniques employed by the eBPA causes a loosely knitted relationship between the neighborhood region being searched (i.e. the neighborhood region of 𝑤𝑜𝑟𝑘𝑖𝑛𝑔) and that of the other solutions registered in the memory structure. The neighborhood region gets redefined upon 𝑤𝑜𝑟𝑘𝑖𝑛𝑔^′ being accepted, as this will become the next 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 solution.

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However, concerning the trajectory of the search, 𝑤𝑜𝑟𝑘𝑖𝑛𝑔^′ solutions get accepted in two ways: if it meets the minimum admittance criterion in being accepted into the memory structure; or, in having been chosen given a certain probabilistic factor. If 𝑤𝑜𝑟𝑘𝑖𝑛𝑔^′ is admitted into the memory structure, and has a fitness value that is better than that of the 𝑤𝑜𝑟𝑠𝑡 solution, then the next 𝑤𝑜𝑟𝑠𝑡 solution will be of an improved quality. If this occurs, the admittance criterion will become more restrictive as the minimum criterion of admittance would increase.

Resultantly, this will also cause the local optimum points to become more clearly defined (see Appendix A for a clearer explanation). With the admittance criterion becoming increasingly restrictive, greater levels of number-crunching would be required to determine further improved solutions. Hence, the decisions made by the admittance criterion is strategically used to influence the behavior of the search.

Within a neighborhood region itself, the ultimate objective is to locate the local optimum point.

However, the eBPA uses intelligence in open-mindedly accepting dis-improved solutions; accepting dis-improved solutions attempts to redirect the search path. This strategy protects against premature convergence in directly attempting to lead away to other neighboring regions. The intent of accepting dis-improved solutions is to balance the effort invested in sifting out the local optimum point from within a local neighborhood region, and in searching for other promising neighborhood regions via exploration. With the neighborhood regions being restructured dynamically, upon updates of the memory structure, the possibility of revisiting previously found solutions remains unlikely.

2.4.3 Exploration and Exploitation of the eBPA

Metaheuristic algorithms are characterized by two important yet contrasting search strategies−

exploration and exploitation (Syam and Al-Harkan, 2010).

Exploration is a global search technique. Its intent is to visit as many neighborhood regions as possible within the confines of the solution space. Ideally, exploration needs to be more influential during the initial phases of the search.

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On the other hand, exploitation is a local search technique. Its intent is to search within a local neighborhood region in trying to locate the local optimum point. Exploitation needs to be more influential during the latter stages of the search, as it aggressively sifts out higher quality solutions.

Striking a balance between exploration and exploitation, throughout the different phases of a search, is critical to the success of any metaheuristic algorithm. Also, this balance is of paramount importance in implementing effective and economical search. Reason being, there is a fine balance between the computational time spent in exploring for the most attractive neighborhood regions, and the computational time spent in exploiting within a local neighborhood region for the optimum point.

The eBPA uses adaptive memory to intelligently control the rate of the transition from exploration to exploitation. During the initial phases of the search, the admittance criterion is less restrictive as the memory structure consists of lower quality solutions; hence, greater levels of exploration is experienced. With the fitness of the solutions in the memory structure being improved upon, with matured search, the admittance criterion becomes increasing restrictive. This allows for greater levels of exploitation to be experienced. Exploitation attempts to incorporate the stronger elements of the 𝑤𝑜𝑟𝑘𝑖𝑛𝑔 solutions into new 𝑤𝑜𝑟𝑘𝑖𝑛𝑔^′ solutions, while discouraging the weaker elements. In performing exploration, the eBPA supposes that strategically accepting dis-improved solutions is more beneficial than a good random solution in influencing the trajectory of the search.

2.4.4 Strategic Reduction of the eBPA Memory Structure

The strategic reduction of the eBPA memory structure is critical to its success. It is also considered strategically more beneficial than maintaining a static memory structure size. The intelligence of strategically reducing the memory structure size will influence greater levels of exploitation as the admittance criterion would constrain further. The advantage of further intensifying exploitation is to place additional pressure in attempting to identify higher quality solutions.

A recommended strategy is to strategically reduce the memory structure size by one, until a memory size of one is reached. Using this technique, every solution admitted into the memory structure will be given a chance to act as the 𝑤𝑜𝑟𝑠𝑡 solution. Therefore, every solution will be given a chance to influence the trajectory of the search.

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The initial size of the memory structure is problem-specific. The results section (i.e. section 2.6) below gives an idea of how to set the memory structure size appropriately.